Contents

What's New
Statistica Help
Technical Support
Useful Features
Edge Scoring Using Boomi AtomSphere®
Feature Finder
Statistica Examples
Advanced Linear and Nonlinear Models
Cox Proportional Hazards Models
Example 1 - Cox Proportional Hazards Model Survival
Example 2 - Cox Proportional Hazards Model Survival - Time-Dependent
Example 3 - Cox Proportional Hazards Model - Models for Recurrent Event Data
Example 4 - Cox Proportional Hazards Model - Building and Deployment to New Data
Fixed Nonlinear Regression
Fixed Nonlinear Regression - Example
Generalized Linear Nonlinear - GLZ Models
GLZ Examples
Example 1: Normal Linear Model with Log Link
Example 2: Binomial Linear Model with Logit Link (Logistic Regression)
Example 3: Ordinal Multinomial Linear Model with Probit Link
Example 4: Multinomial Linear Model with Generalized Logit Link
Example 5: Tweedie Distribution with Log Link
GLZ Syntax Examples
GLZ Syntax - Example 1: Multiple Regression with Categorical Predictors
GLZ Syntax - Example 2: Stepwise Multiple Regression
GLZ Syntax - Example 3: Best Subset Regression with Multinomial Response
GLZ Syntax - Example 4: Factorial Design and Binomial Response
General Linear Models (GLM)
GLM Basic Examples
Example 1: A 2 x 3 Between-Groups Factorial ANOVA Design
Example 2: One-Way ANOVA Design
Example 3: Main Effects ANOVA Design
Example 4: Mixed-Model Factorial ANOVA Design
Example 5: Mixed-Model Nested ANOVA Design
Example 6: Repeated Measures ANOVA Design
Example 7: Simple Regression Analysis
Example 8: Multiple Regression Analysis
Example 9: Response Surface Design
Example 10: Mixture Surface Design
Example 11: MANCOVA Design
GLM Advanced Examples
Advanced Example 1: Type V Sums of Squares
Advanced Example 2: Planned Comparisons of Least Squares Means
Advanced Example 3: Custom Within Designs
Advanced Example 4: Post-Hoc Tests for Repeated Measures
Advanced Example 5: Specifying a Process-Mixture Design
Advanced Example 6: Response Optimization
Advanced Example 7: Cross-Validation
GLM Syntax Examples Overview
GLM Syntax - Example 1: 4-Way Between Group Design
GLM Syntax - Example 2: Between-within (repeated measures) design
GLM Syntax - Example 3: Hierarchically Nested Design with Random Effects
GLM Syntax - Example 4: Multiple Regression
GLM Syntax - Example 5: 2(k-p) Fractional Factorial
GLM Syntax - Example 6: Response Surface Design and Lack of Fit
GLM Syntax - Example 7: Mixture Design
GLM Syntax - Example 8: Mixture/Process Design
GLM Syntax - Example 9: Multivariate Repeated Measures
GLM Syntax - Example 10: Equality of Regression Coefficients
GLM Syntax - Example 11: Cross-over design
General Partial Least Squares (PLS) Models
PLS Examples
Example 1: Multiple Regression
Example 2: Multivariate Regression
PLS Syntax - Examples
PLS Syntax - Example 1: Multiple Regression with Categorical Predictors
PLS Syntax - Example 2: Multivariate Regression with Categorical Predictors
PLS Syntax - Example 3: Multivariate Regression with SIMPLS
General Regression Models (GRM)
General Regression Models (GRM) - Examples
Example 1: Stepwise Regression
Example 2: Best-Subset Regression
GRM Syntax Examples
GRM Syntax - Example 1: Multiple Regression
GRM Syntax - Example 2: Stepwise Multiple Regression with Categorical Predictors
GRM Syntax - Example 3: Best-Subset Regression with Categorical Predictors
GRM Syntax - Example 4: Testing Equality of Regression Coefficients
Log Linear
Example 1: Specifying Tables Directly (Frequency Table Input)
Example 2: Log Linear Analysis of Frequency Tables (incomplete)
NIPALS Algorithm (PCS - PLS)
PCA Example
Nonlinear Estimation
Probit and Logit Models
Example 1: Predicting Success/Failure
Example 2: Predicting Redemption of Coupons
Exponential Regression Models
Example 3: Predicting Recovery from Injury
Example 4: Comparing Two Learning Curves
Example 4.1: Estimating Two Different Models
Other Regression Models
Example 5: Regression in Pieces
Example 6: Estimating Drug Responsiveness (Half-Maximal Response)
Specifying Loss Functions
Example 7: Weighted Least Squares
Nonlinear Estimation Syntax for User Functions
Example: Specifying a Regression Model that Will Evaluate Differences Across Groups
Example: Using Logical Expressions to Estimate Regression Parameters
Nonlinear Estimation Examples - Concluding Remarks
SEPATH
Introductory Example - Confirmatory Factor Analysis
Example 1: Stability of Alienation
Example 2: A Confirmatory Factor Analysis
Example 3: Confirmatory Factor Analysis with Identifying Constraints
Example 4: Effect of Peer Influence on Ambition
Example 5: Standardized Solutions for the Effect of Peer Influence on Ambition
Example 6: Factor Analysis with an Intercept Variable
Example 7: Comparing Factor Structure in Two Groups
Example 8: Testing for Circumplex Structure
Example 9: Testing for Stability of a Correlation Matrix over Time
Example 10: A Multiple Regression Model for Home Environment and Math Achievement
Example 11: Structural Models for Home Environment and Mathematics Achievement
Example 12: Test Theory Models for Sets of Congeneric Tests
Example 13: Comparing Dependent Variances
Example 14: A Multi-Trait, Multi-Method Model
Example 15: A Longitudinal Factor Model
Example 16: A Structural Model for 10 Personality and Drug Use Variables
Example 17: A Test for Compound Symmetry
Example 18: Testing the Equality of Correlation Matrices from Different Populations
Survival Analysis Examples - Overview and Data File
Example 1: Actuarial Life Table
Example 2: Kaplan-Meier Product-Limit Estimates
Example 3: Comparing Survival in Two or More Groups
Example 4: Regression Models
Example 5: Cox Model with Time-Dependent Covariates
Time Series
Example 1: Transformation of Variables
Example 2: Single Series ARIMA
Example 3: Interrupted ARIMA
Example 4: Seasonal and Non-seasonal Exponential Smoothing
Example 5: Seasonal Decomposition (Census Method I)
Example 6: X-11 Seasonal Decomposition (Census Method II)
Example 7: Distributed Lags Analysis
Example 8: Spectrum (Fourier) Analysis
Variance Components
Example 1: Variance Component Estimation for a Two-Way Random Factorial Design
Example 2: Variance Component Estimation for a Four-Way Mixed Factorial Design
Example 3: Variance Component Estimation for a Hierarchically Nested Random Design
Example 4: Estimating the Population Intraclass Correlation via Variance Components
Basic Statistics
Example 1: Descriptive Statistics, t-Tests, and Correlations
Example 2: Breakdown and One-Way ANOVA
Example 3: Frequency Tables
Example 4: Stub-and-Banner Tables
Example 5: Crosstabulation Tables
Example 6: Tabulating Multiple Responses and Dichotomies
Example 7: Detecting Outliers
Example 8: Visually Comparing Variables
Example 9: Using Contingency Tables to Compute Chi-Square Tests for Independence
Example 10: Chi-Square Test for Independence
Batch (ByGroup) Analysis Example
Customizing Statistica
Getting Started
Assign/Remove Keyboard Shortcut Keys
Create a New Toolbar
Customizing Toolbars
Customizing Menus
Toolbar and Menu Appearance
Data Mining
Example 1: A simple project for creating standard reports and summaries
Example 2: Visual Data Mining
Example 3: Predictive data mining and deployment for a continuous output variable
Example 4: Predictive Data Mining for Categorical Output Variable (Classification)
Example 1: Association Rules Applied to Consumer Preferences
Boosted Trees
Example 1: Classification via Boosted Trees
Example 2: Prediction of Continuous Dependent Variable
Developing Credit Scoring Model for Data Miner Recipe - Example
Example 1: CHAID Classification Tree
General Classification and Regression
Example 1: Pattern Recognition (Classification of Digits)
Example 2: Regression Tree for Predicting Poverty
Generalized Additive Models Example
Generalized EM and k-Means Cluster Analysis
Example 1: Automatic selection of the best number of clusters from the data
Example 2: Illustration of EM Clustering with a synthetic data set
Example: Goodness of Fit Indices for Regression Predictions
Independent Component Analysis Example
Interactive Trees (C&RT, CHAID) Example
Machine Learning
k-Nearest Neighbors
K-Nearest Neighbor Example 1 - Classification
K-Nearest Neighbor Example 2 - Regression
Naive Bayes Classifier Example - Classification
Support Vector Machines
Support Vector Machine Example 1 - Classification
Support Vector Machine Example 2 - Regression
MARSplines Example
Example: Using SIC Codes for Building a Predictive Model
Process Optimization
General Optimization Example
Quality Control Charts
QC Charts Example 1: Specifying Control Charts for Variables, Process Capability
QC Charts Example 2: Brushing, Assigning Causes and Actions
QC Charts Example 3: Computing Chart Parameters for Different Sets
QC Charts Example 4: Setting Up Shop Floor Mode and Alarm Notification
QC Charts Example 5: Specifying Control Charts for Attributes, Custom Control Limits
QC Charts Example 6: Specifying Pareto Charts, Changing the Project Header, Printing Results
QC Charts Example 7: Specifying Moving Average/Moving Range charts
QC Charts Example 8: Specifying Charts for Individual Observations and Moving Ranges
QC Charts Example 9: Using IQC on the Shop Floor
QC Charts Example 10: Specifying Control Charts for Short Production Runs
QC Charts Example 11: Specifying CuSum Charts
Response Optimization
Response Optimization Example - Classification
Response Optimization Example - Regression
Random Forests
Example: Classification Random Forests
Example: Regression Random Forests
Rapid Deployment of Predictive Models
Example 1: Creating Deployment Code for Scoring New Records
Example 2: Rapid Deployment of Predictive Models
Sequence, Association, and Link Analysis
Example - Association Rules Applied to Consumer Preferences
Example - Sequence Association Rules Applied to Microsoft Web Logs
Statistica Automated Neural Networks (SANN)
SANN Example 1: Performing Regression with 4-Bar Linkage Data
SANN Example 2: The Iris Problem (Classification)
SANN Example 3: Growth in Number of Airline Passengers over Time
SANN Example 4: Time Series (Classification)
SANN Example 5: Cluster Analysis in SANN
SANN Example 6: Deploying the 4Bar Linkage Data
Text and Document Mining, Web Crawling
Automatic Classification of Text
Refining Automatic Text Classification with Synonyms and Phrases
Weight of Evidence (WoE) example
Distribution Fitting Example
Distributions & Simulation Example
Extract, Transform, and Load - ETL
Example 1: Aligning Data From a Cyclone Furnace
Example 2: Comparing Historical Stock prices
Example 3: Aligning Multiple Stages of Process Data
General ANOVA MANOVA
Example 1: Simple Factorial ANOVA with Repeated Measures
Example 2: A 2 x 3 Between-Groups ANOVA Design
Example 3: A 2-Level Between-Group x 4-Level Within-Subject Repeated Measures Design
ANOVA/MANOVA Syntax - Examples
Graphs
Example: Creating a Graph of Block Data Using an Analysis Output Spreadsheet as Input
Example: Creating a Graph of Block Data Using an Input Data File
Example: Creating a Graph of Input Data
Example: Creating and Customizing a Simple 2D Graphs Menu Graph
Example: Creating a Wafer Plot
Specifying Multiple Subsets Example
Industrial Statistics
Experimental Design
Example 1.1: Designing and Analyzing a 2(7-4) Fractional Factorial Design
Example 1.2: Analyzing a 26 Full Factorial
Example 1.3: Analyzing a Botched 2(7-4) Fractional Factorial Design
Example 2: Designing and Analyzing a 35-Factor Screening Design
Example 3: Analyzing a 33 Full Factorial
Example 4: Designing and Analyzing a 2332 Experiment
Example 5: Central Composite (Response Surface) Designs
Example 6: Latin Square Designs
Example 7: Taguchi Robust Design Experiment
Example 8.1: Designing and Analyzing a Mixture Experiment
Example 8.2: Designing and Analyzing a Mixture Experiment with Pseudo-Components
Example 9.1: Finding Vertex and Centroid Points for a Constrained Region
Example 9.2: Mixture Designs for Components with Upper and Lower Bound Restrictions
Example 10.1: Constructing a Simple D-Optimal Design
Example 10.2: Constructing a Design from Vertex and Centroid Points (Example 9.1, Continued)
Example 11: Constructing a D-Optimal Split Plot Design
Special Topics Example 1 - Simultaneous Optimization of Several Response Variables in a Central Composite (Response Surface) Design
Special Topics Example 2 - Optimization of the Response Variable in a Three-Factor Mixture Experiment
Special Topics Example 3 - Residuals Analysis
Special Topics Example 4 - Box-Cox Transformation of a Dependent Variable
Multivariate Quality Control
MQC Charts Example 1 - Creating a Hotelling's T2 Chart
MQC Charts Example 2 - Creating a MEWMA Chart for Individuals
MQC Charts Example 3 - Computing Chart Parameters for Different Sets
MQC Charts Example 4 - Creating a Multiple Stream Chart
MQC Charts Example 5 - Creating a Multivariate CUSUM Chart
Process Analysis
Introduction to Process Analysis Examples
Example 1: Deriving and Examining a Sampling Plan
Example 2: Process Capability Analysis
Example 3: Gage Repeatability and Reproducibility
Example 4: Weibull and Reliability/Failure Time Analysis
Example 5: Gage Linearity and Bias Study
Example 6: Attribute Agreement Analysis
Example 7: Capability Ratios for True Position
Example 8: MSA Attribute Data
Example 9: Attribute Gage Study (Analytic Method)
Example 10: Capability Analysis (Binomial)
Example 11: Unstacking Data in Attribute Agreement Analysis
Quality Control Charts
QC Charts Example 1: Specifying Control Charts for Variables, Process Capability
QC Charts Example 2: Brushing, Assigning Causes and Actions
QC Charts Example 3: Computing Chart Parameters for Different Sets
QC Charts Example 4: Setting Up Shop Floor Mode and Alarm Notification
QC Charts Example 5: Specifying Control Charts for Attributes, Custom Control Limits
QC Charts Example 6: Specifying Pareto Charts, Changing the Project Header, Printing Results
QC Charts Example 7: Specifying Moving Average/Moving Range charts
QC Charts Example 8: Specifying Charts for Individual Observations and Moving Ranges
QC Charts Example 9: Using IQC on the Shop Floor
QC Charts Example 10: Specifying Control Charts for Short Production Runs
QC Charts Example 11: Specifying CuSum Charts
Macro (SVB) Program Examples
Monte Carlo
Monte Carlo Example 1: Adequacy of Sample Size and Heywood Cases in Factor Analysis
Monte Carlo Example 2: Performance of GLS and ML Estimation in the Comparison of Correlation Matrices
Monte Carlo Example 3: Bootstrapping
Multiple Regression
Example 1: Standard Regression Analysis
Example 2: Stepwise Regression Analysis
Multivariate Exploratory Techniques
Example1: Canonical Correlation
Classification Trees
Example 1: Discriminant-Based Univariate Splits for Categorical Predictors
Example 2: Discriminant-Based Univariate Splits for Categorical and Ordered Predictors
Example 3: Discriminant-Based Linear Combination Splits for Ordered Predictors
Example 4: Exhaustive Search for Univariate Splits for Ordered Predictors
Cluster Analysis
Example 1: Joining - Tree Clustering
Example 2: K-means Clustering
Correspondence Analysis
Example 1: Correspondence Analysis and Supplementary Points
Example 2.1: Analyzing an Indicator Matrix (Consumer Preferences)
Example 2.2: Multiple Correspondence Analysis
Example 3: Protein Consumption in Europe
Discriminant Function Analysis - Example
Example 1: Factor Analysis
General Discriminate Analysis - GDA
GDA Examples
General Discriminant Analysis (GDA) Models - Examples
GDA - Example 1: Stepwise Discriminant Analysis
GDA - Example 2: Best-Subset Discriminant Analysis
GDA Syntax - Examples
GDA Syntax - Example 1: Simple Standard Discriminant Analysis
GDA Syntax - Example 2: Stepwise Discriminant Analysis with Categorical Predictors
Multidimensional Scaling - Example
Principal Components & Classification Analysis Example
Reliability and Item Analysis
Example 1: Evaluating the Reliability of Items in a Questionnaire
Example 2: Split-Half Reliability
Multivariate Statistical Process Control (MSPC)
PCA Example
PCA Deployment Example
TMPCA Example
MSPC Raw Data Example
Nonparametrics
Example 1: 2 x 2 Tables
Example 2: Sign Test
Example 3: Observed vs. Expected Chi-Square
Example 4: Correlations
Example 5: Wald-Wolfowitz Runs Test, Mann-Whitney U Test, Kolmogorov-Smirnov Two-Sample Test
Example 6: Kruskal-Wallis ANOVA & Median Test
Example 7: Friedman ANOVA & Kendall Concordance
Example 8: Wilcoxon Matched Pairs Test
Example 9: Cochran Q Test
Example 10: Descriptive Statistics
PLS, PCA, Multivariate Batch SPC
Principal Components Analysis (PCA)
PCA Example
PCA Deployment Example
TMPCA Example
Power Analysis
Example 1: Power and Sample Size Calculation for the Independent Sample t-Test
Example 2: Analyzing Power, Sample Size, and Effect Size in 1-Way ANOVA
Example 3: Sample Size Calculation in Factor Analysis
Example 4: Power and Sample Size in Complex Factorial ANOVA
Example 5: Power Calculation in a 1-Way Repeated Measures ANOVA
Example 6: Hypotheses About the Noncentrality Parameter of the F Distribution
Example 7: Constructing a Confidence Interval on the Noncentrality Parameter
Example 8: Power of Nonstandard Significance Tests in the Analysis of Variance
Example 9: Exact Tests and Confidence Intervals for the Correlation Coefficient
Example 10: Confidence Intervals and Special Tests on the Multiple Correlation
Query
Example - Querying a Database Using ODBC
Example - Querying a Database Using OLE DB
Statistica Visual Basic
Macro (SVB) Program Examples
Getting Started
A Simple Statistica Visual BASIC (SVB) Program
A Simple Message Box, and If..Then..End If Block
Spreadsheets and Statistics
Macro (SVB) Programs Example - Basic Spreadsheet Manipulation
Macro (SVB) Program Examples - Creating a Random Sized Spreadsheet
Macro (SVB) Programs Example - Resize a Spreadsheet
Macro (SVB) Programs Example - Auto-Fitting a Spreadsheet's Variable Columns
Macro (SVB) Programs Example - Converting a Spreadsheet's Data Type
Macro (SVB) Programs Example - Creating Sample Statistics (Descriptives and Correlations)
Macro (SVB) Programs Example - Creating a Custom Spreadsheet
Macro (SVB) Programs Example - Formatting Cells in Spreadsheets
Macro (SVB) Programs Example - Accessing Results Spreadsheet Data
Macro SVB Programs Examples - Searching a Spreadsheet's Cells for a Value
Macro (SVB) Programs Example - Inserting an Array into a Spreadsheet
Macro (SVB) Programs Example - Retrieving a Collection of Spreadsheets
Macro (SVB) Programs Example - Customizing a Simple Descriptive Statistics Macro
Macro (SVB) Programs Example - Accessing Multiple Modules in a Single Program
Macro (SVB) Programs Example - Creating a Cell-Function Spreadsheet
Macro (SVB) Programs Example - Creating a Self-Updating Dataset
Graphs
Macro (SVB) Programs Example - Creating and Customizing Box-Plots
Macro (SVB) Programs Example - Accessing and Customizing Graphs
Macro (SVB) Programs Example - Creating User-Defined Custom Functions
Macro (SVB) Programs Example - Customizing Graphs
Macro (SVB) Programs Example - Creating and Customizing Graph Objects
Macro (SVB) Programs Example - Graph Function Library
Reports
Macro (SVB) Programs Example - Customizing Reports
Macro (SVB) Programs Example - Copying Cells from a Spreadsheet into a Report
Workbooks
Macro (SVB) Programs Example - Customizing Workbooks
Macro (SVB) Programs Example - Inserting All Open Windows into a Workbook
Statistica Interface
Macro (SVB) Programs Example - Launching a New Statistica Application
Macro (SVB) Programs Example - Adding a Custom Toolbar via a SVB Macro Program
Macro (SVB) Programs Example - Opening and Saving Statistica Documents
Macro (SVB) Programs Example - Printing Statistica Documents
Macro (SVB) Programs Example - Statistica Dialog Box for Selecting Data Files
Macro (SVB) Programs Example - Statistica Dialog Boxes for Selecting Variables
Macro (SVB) Programs Example - Writing a Custom Statistica Application with User-Defined Dialog Boxes
Macro (SVB) Programs Example - Displaying a Progress Bar
Statistica Query
Macro (SVB) Program Examples - Creating a Statistica Query Toolbar
Macro (SVB) Program Examples - Running a Query via a Macro
Other Environments
Macro (SVB) Programs Example - Accessing Statistica Visual Basic Functions from Microsoft Excel
Macro (SVB) Programs Example - Accessing Statistica Visual Basic Functions from Microsoft Word
Limitations and Differences between SVB and Visual Basic in Other Applications
Variance Estimation and Precision
Example 1: Balanced and Unbalanced Two-Way Mixed Models
Example 2: Variance Component Estimation for a Four-Way Mixed Factorial Design
Example 3: Variance Component Estimation for a Hierarchically Nested Random Design
Example 4: Estimating the Population Intraclass Correlation in Variance Estimation and Precision
Example 5: Analyzing a Nested Mixed Model
Workbooks
Creating a Workbook
Adding Items to a Workbook
Adding the Current Item to a Workbook
Adding a Folder to a Workbook
Adding a New Statistica Document to a Workbook
Adding a Saved Statistica Document to a Workbook
Adding a New ActiveX Document to a Workbook
Adding a Previously Saved Object to a Workbook
Deleting Items from a Workbook
Extracting a File from a Workbook
Opening a Workbook
Printing a Workbook
Rearranging the Workbook Tree with Drag-and-Drop
Renaming Workbook Items
Saving a Workbook
Using Copy and Paste in a Workbook
Statistica Enterprise Manager Examples
Examples Overview
Example 1: Setting Up the System View
Example 2: Setting Up Enterprise Users and Groups
Example 3: Setting Up a Database Connection
Example 4: Setting Up a Data Configuration
Example 5: Setting Up a QC Analysis Configuration
Example 6: Setting Up a Report Configuration
Example 7: Setting Up Server Tasks
Example 8: Setting Up Dashboards
Example 9: Displaying All Acknowledged Dashboard Alarms
Example 10: Setting Up a Data Entry Setup
Advanced Examples
Data Filtering
Email Alarm Notification
Cause and Action Write-Backs
Automatically Include Filtering Conditions in QC Charts
Help - R Integration with Statistica
Options - R Integration
Custom R Workspace Nodes
Statistics - Analyzing Data
Commonly Used Statistical Tests
Elementary Concepts
Probability Distribution Calculator - Overview
Statistical Advisor
Data Mining
Association Rules Overview
Association Rules - Computational Procedures and Terminology
Association Rules - Tabular Representation of Associations
Association Rules - Graphical Representation of Associations
Association Rules - Interpreting and Comparing Results
Association Rules - Technical Note on Coding of Multiple Response Variables
Association Rules Startup Panel
Association Rules Startup Panel - Quick Tab
Association Rules Startup Panel - Advanced Tab
Association Rules Results
Association Rules Results - Quick Tab
Association Rules Results - Advanced Tab
Example 1: Association Rules Applied to Consumer Preferences
Statistica Automated Neural Networks (SANN) - Neural Networks Overview
SANN Overviews - Neural Network Tasks
SANN Overviews - Network Types
SANN Overviews - Activation Functions
SANN Overviews - Selecting the Input Variables
SANN Overviews - Neural Network Complexity
SANN Overviews - Network Training
SANN Overviews - Network Generalization
SANN Overviews - Pre and Post Processing of Data
SANN Overviews - Predicting Future Data and Deployment
SANN Overviews - Recommended Textbooks
SANN Overviews - Ensembles and Subsampling
SANN - New Analysis/Deployment Startup Panel and Quick Tab
SANN - Data Selection
SANN - Data Selection - Quick Tab
SANN - Data Selection - Sampling (CNN and ANS) Tab
SANN - Data Selection - Subsampling Tab
SANN - Data Selection - Time Series Tab
SANN - Automated Network Search (ANS)
SANN - Automated Network Search (ANS) - Quick Tab
SANN - Automated Network Search (ANS) - MLP Activation Functions Tab
SANN - Automated Network Search (ANS) - Weight Decay Tab
SANN - Automated Network Search (ANS)  Initialization Tab
Neural Network Training in Progress
SANN - Custom Neural Network
SANN - Custom Neural Network - Quick Tab
SANN - Custom Neural Network/Subsampling - MLP Tab
SANN - Custom Neural Network/Subsampling - RBF Tab
SANN - Custom Neural Network/Subsampling - Weight Decay Tab
SANN - Custom Neural Network/Automated Network Search (ANS)/Subsampling - Initialization Tab
SANN - Custom Neural Network/Subsampling - Real Time Training Graph Tab
SANN - Custom Neural Network - Quick (Kohonen) Tab
SANN - Custom Neural Network - Kohonen Training Tab
SANN - Subsampling Dialog Box and Quick Tab
SANN - Subsampling - MLP Tab
SANN - Subsampling - RBF Tab
SANN - Subsampling – Weight Decay Tab
SANN - Subsampling - Initialization Tab
SANN - Custom Neural Network/Subsampling - Real Time Training Graph Tab
SANN - Results
SANN - Results - Predictions Tab
SANN - Results - Graphs Tab
SANN - Results - Details Tab
SANN - Results - Custom Predictions Tab
SANN - Results - Predictions (Kohonen) Tab
SANN - Results - Graphs Tab
SANN - Results - Kohonen Graph Tab
SANN - Results - Liftcharts Tab
SANN - Results - Time Series Tab
Model Activation
Model Deletion
Examples
SANN Example 1: Performing Regression with 4-Bar Linkage Data
SANN Example 2: The Iris Problem (Classification)
SANN Example 3: Growth in Number of Airline Passengers over Time
SANN Example 4: Time Series (Classification)
SANN Example 5: Cluster Analysis in SANN
SANN Example 6: Deploying the 4Bar Linkage Data
Boosted Trees Overview
Boosted Trees for Regression and Classification Overview (Stochastic Gradient Boosting) - Basic Ideas
Boosted Trees Startup Panel and Quick Tab
Boosted Trees Specifications
Boosted Trees Specifications - Quick Tab
Boosted Trees Specifications - Advanced Tab
Boosted Trees Specifications - Classification Tab
Boosted Trees Results
Boosted Trees Results - Quick Tab
Boosted Trees Results - Classification Tab
Boosted Trees Results - Prediction Tab
Boosted Trees Results - Report Tab
Example 1: Classification via Boosted Trees
Example 2: Prediction of Continuous Dependent Variable
Generalized EM and k-Means Cluster Analysis Overview
Cluster Analysis
Cluster Analysis - Quick tab
Cluster Analysis - k-Means tab
Cluster Analysis - EM tab
Cluster Analysis - Tree Clustering Tab
Cluster Analysis - Validation Tab
Cluster Analysis Results
Generalized Cluster Analysis Results - Quick tab
Generalized Cluster Analysis Results - Advanced tab
Tree Clustering Results - Quick Tab
Tree Clustering Results - Advanced Tab
Example 1: Automatic selection of the best number of clusters from the data
Example 2: Illustration of EM Clustering with a synthetic data set
Technical Notes: The k-Means Algorithm
Technical Notes: The EM Algorithm
Data Miner Recipes (DMR) Overview
Data Miner Recipes UI
Steps Tab
Options Tab
Data Preparation
Data Preparation Tab
Data Preparation - Advanced Tab
Annotations Tab
Data for Analysis
Data for Analysis Tab
Annotations Tab
Data Redundancy
Data Redundancy Tab
Annotations Tab
Target Variable
Target Variable- Important Variables Tab
Annotations Tab
Model Building
Model Building Tab
C&RT
Random Forest
Boosting Trees
Neural Networks
Support Vector Machine
Annotations Tab
Evaluation
Evaluation Tab
Annotations Tab
Deployment
Deployment Tab
Annotations Tab
Developing Credit Scoring Model for Data Miner Recipe - Example
Data Mining Workspaces
Interactive Drill-Down Explorer
Interactive Drill-Down Explorer Overview
Auto-Updating Graphs and Summary Statistics after Each Drill-Down
Dialogs
Interactive Drill-Down Explorer Startup Panel
Interactive Drill-Down Explorer Startup Panel - Quick Tab
Interactive Drill-Down Explorer Startup Panel - Categorization Tab
Interactive Drill-Down Explorer Startup Panel - Review Tab
Interactive Drill-Down Explorer Startup Panel - Options Tab
Drill-Down Order
Select Category
Specify Boundaries
Data Mining ... Quick Linear Models Project
Data Mining – Data Mining - Workspaces – Data Miner - General Classifier (Trees and Clusters) – Advanced Comprehensive Classifiers Project
Data Mining - Data Mining - Workspaces - Data Miner - General Modeler and Multivariate Explorer - Advanced Comprehensive Regression Models
Data Mining - Data Mining - Workspaces - Data Miner - General Forecaster - Neural Network Time Series Project
Data Mining - Data Mining - Workspaces - Data Miner - General Neural Network Explorer - Multilayer Perceptrons Project
Data Mining - Data Mining - Workspaces - Data Miner - General Neural Network Explorer - Advanced Multiple Architectures Project
Feature Selection and Variable Screening
Overviews
Feature Selection and Variable Screening Overview
Feature Selection and Variable Screening - Computational Details
Feature Selection and Variable Screening and Quick Tab
FSL Results (Feature Selection and Variable Screening Results)
General CHAID Overview
Basic Tree-Building Algorithm: CHAID and Exhaustive CHAID
General Computation Issues and Unique Solutions of Statistica GCHAID
CHAID, C & RT, and QUEST
Missing Data in GC&RT, GCHAID, and Interactive Trees
General CHAID Models Startup Panel and Quick Tab
General CHAID Analysis Syntax Editor
Analysis Syntax Editor - Keywords Tab
Analysis Syntax Editor - Specifications Tab
Alternative Method for Specifying GLM Designs in Macros (Automation)
General CHAID Analysis Wizard--Between Design
General CHAID Analysis Wizard--Between Design - Default Between Design Tab
General CHAID Analysis Wizard--Between Design - Custom Between Design Tab
General CHAID Analysis Wizard--Extended Options
General CHAID Analysis Wizard--Extended Options - Quick Tab
General CHAID Analysis Wizard--Extended Options - Classification Tab
General CHAID Analysis Wizard--Extended Options - Stopping Tab
General CHAID Analysis Wizard--Extended Options - Validation Tab
General C&R Trees Wizard - Extended Options - Advanced Tab
General CHAID Models Quick Specs
General CHAID Models Quick Specs - Quick Tab
General CHAID Models Quick Specs - Classification Tab
General CHAID Models Quick Specs - Stopping Tab
General CHAID Models Quick Specs - Validation Tab
Cross-Validation
General CHAID Models Quick Specs - Advanced Tab
GCHAID Results
GCHAID Results - Summary Tab
GCHAID Results - Observational Tab
GCHAID Results - Classification Tab
GCHAID Results - Node Tab
GCHAID Results - Report Tab
Example 1: CHAID Classification Tree
General Classification and Regression Trees Introductory Overview
GC&RT Introductory Overview - Basic Ideas Part II
Classification and Regression Trees (C&RT) - Computational Details
Computational Formulas
Missing Data in GC&RT, GCHAID, and Interactive Trees
Predictor Importance in Statistica GC&RT, Interactive Trees, and Boosted Trees
General Classification and Regression Trees Startup Panel
General Classification and Regression Trees Startup Panel and Quick Tab
General Classification/Regression Tree Models Syntax - Overview
General Classification and Regression Trees Syntax Editor
Analysis Syntax Editor - Keywords Tab
Analysis Syntax Editor - Specifications Tab
Alternative Method for Specifying GLM Designs in Macros (Automation)
GC&RT Analysis Wizard - Between Design
GC&RT Analysis Wizard - Between Design - Custom Between Design Tab
GC&RT Analysis Wizard Between Design - Default Between Design Tab
General C&R Trees Wizard - Extended Options
General C&R Trees Wizard - Extended Options - Quick Tab
General C&R Trees Wizard - Extended Options - Classification Tab
General C&R Trees Wizard - Extended Options - Stopping Tab
General C&R Trees Wizard - Extended Options - Validation Tab
General C&R Trees Wizard - Extended Options - Advanced Tab
C&RT Quick Specs
C&RT Quick Specs - Quick Tab
C&RT Quick Specs - Classification Tab
C&RT Quick Specs - Stopping Tab
C&RT Quick Specs - Validation Tab
Cross - Validation
C&RT Quick Specs - Advanced Tab
GC&RT Results
GC&RT Results - Summary Tab
GC&RT Results - Observational Tab
GC&RT Results - Classification Tab
GC&RT Results - Node Tab
GC&RT Results - Report Tab
Example 1: Pattern Recognition (Classification of Digits)
Example 2: Regression Tree for Predicting Poverty
Generalized Additive Models Overview
Generalized Additive Models - Program Overview
Generalized Additive Models Startup Panel
Generalized Additive Models Startup Panel and Quick Tab
GAM Specifications
GAM Specifications - Options Tab
GAM Specifications - Quick Tab
Degree of Freedom Values
GAM Summary Results Spreadsheets and Graphs
Generalized Additive Models Example
Goodness of Fit Calculations Overview
Goodness of Fit, Classification, Prediction Startup Panel
Goodness of Fit, Classification, Prediction Startup Panel - Quick Tab
Goodness of Fit, Classification, Prediction Startup Panel - Advanced Tab
Goodness of Fit Results
Goodness of Fit Results - Quick Tab
Goodness of Fit Results - Advanced Tab
Example: Goodness of Fit Indices for Regression Predictions
Goodness of Fit Computational Details
Independent Component Analysis Overview
Fast Independent Component Analysis Startup Panel
Fast Independent Component Analysis Startup Panel - Quick Tab
Fast Independent Component Analysis Startup Panel - Options Tab
Fast Independent Component Analysis Startup Panel - Deployment Tab
Fast Independent Component Analysis Results
Fast Independent Component Analysis Results Dialog - Quick Tab
Fast Independent Component Analysis Results Dialog - Advanced Tab
Independent Component Analysis Example
Independent Component Analysis Technical Notes
Interactive Trees (C&RT, CHAID) Overview
Comparison of Interactive Trees and GC&RT and GCHAID
Program Overview
Missing Data in GC&RT, GCHAID, and Interactive Trees
Interactive Trees Startup Panel
Interactive Trees Startup Panel and Quick Tab
ITrees Extended Options
ITrees Extended Options - Quick Tab
ITrees Extended Options - Classification Tab
ITrees Extended Options - Stopping Tab
ITrees Extended Options - Validation Tab
ITrees Extended Options - Advanced Tab
Options for C&RT
Options for CHAID and Exhaustive CHAID
Intervals
Cross-Validation
ITrees Results
ITrees Results - Manager Tab
ITrees Results - Summary Tab
ITrees Results - Classification Tab
ITrees Results - Prediction Tab
ITrees Results - Report Tab
Interactive Trees (C&RT, CHAID) Example
Stepwise Model Builder - Cox Regression Introductory Overview
Stepwise Model Builder - Cox Regression
Marginal Statistics for Stepwise Model Builder - Cox Regression
Model Statistics for Stepwise Model Builder - Cox Regression
Stepwise Model Builder - Linear Regression Introductory Overview
Stepwise Model Builder - Linear Regression
Marginal Statistics for Stepwise Model Builder - Linear Regression
Model Statistics for Stepwise Model Builder - Linear Regression
Lasso Regression
Lasso Regression and Quick Tab
Quick tab
Lasso Regression - Advanced Tab
Set Penalties for Predictors
Lasso Regression - Validation Tab
Lasso Regression Results and Quick Tab
Quick tab
Lasso Regression Results - Advanced Tab
Sequence, Association and Link Analysis Module Overview
Sequence, Association, & Link Analysis (SAL) Technical Notes
Link Analysis Startup Panel
Quick Tab
Advanced Tab
Sequence Tab
Taxonomy Tab
Nonsequential Tab
Project Tab
Assign Item Names and Synonyms to Variables
Link Analysis Results
Quick Tab
Advanced Tab
Visualization Tab
Deployment Tab
Example - Association Rules Applied to Consumer Preferences
Example - Sequence Association Rules Applied to Microsoft Web Logs
Machine Learning Program Overview
Machine Learning Startup Panel and Quick Tab
Support Vector Machines Introductory Overview
Support Vector Machines
Support Vector Machines - Quick Tab
Support Vector Machines - Sampling Tab
Support Vector Machines - SVM Tab
Support Vector Machines - Kernels Tab
Support Vector Machines - Cross-Validation Tab
Support Vector Machines - Training Tab
Penalty Selection
Penalty Tab
Support Vector Machine Results
Support Vector Machine Results - Quick Tab
Support Vector Machine Results - Plots Tab
Support Vector Machine Results - Custom Predictions Tab
Support Vector Machine Example 1 - Classification
Support Vector Machine Example 2 - Regression
Naive Bayes Classifier Introductory Overview
Naive Bayes
Naive Bayes - Quick Tab
Naive Bayes - Sampling Tab
Naive Bayes - Distributions Tab
Naive Bayes - Memory Usage Tab
Naive Bayes Classifier Results
Naive Bayes Classifier Results - Quick Tab
Naive Bayes Classifier Results - Plots Tab
Naive Bayes Classifier Results - Prior Tab
Naive Bayes Classifier Results - Posterior Tab
Naive Bayes Classifier Results - Custom Predictions Tab
Naive Bayes Classifier Example - Classification
k-Nearest Neighbors Introductory Overview
K-Nearest Neighbors
K-Nearest Neighbors - Quick Tab
K-Nearest Neighbors - Sampling Tab
K-Nearest Neighbors - Options Tab
K-Nearest Neighbors - Cross-Validation Tab
K-Nearest Neighbors - Memory Usage Tab
Sampling Variable
K-Nearest Neighbors Results
K-Nearest Neighbors Results - Quick Tab
K-Nearest Neighbors Results - Plots Tab
K-Nearest Neighbors Results - Options Tab
K-Nearest Neighbors Results - Custom Predictions Tab
K-Nearest Neighbor Example 1 - Classification
K-Nearest Neighbor Example 2 - Regression
Model Converter
Multivariate Adaptive Regression Splines (MARSplines) Overview
Multivariate Adaptive Regression Splines (MARSplines) Startup Panel
Multivariate Adaptive Regression Splines (MARSplines) Startup Panel - Quick Tab
Multivariate Adaptive Regression Splines (MARSplines) Startup Panel - Options Tab
Multivariate Adaptive Regression Splines (MARSplines) Results
MARSplines Results - Quick Tab
MARSplines Results - Plots Tab
MARSplines Results - Save Tab
MARSplines Results - Custom Predictions Tab
Specify Input Values
MARSplines Example
Technical Notes: The Multivariate Adaptive Regression Splines (MARSplines) Model
Technical Notes: The MARSplines Algorithm
Optimal Binning for Predictive Data Mining Overview
Optimal Binning for Predictive Data Mining Program Overview
Optimal Binning Startup Panel
Optimal Binning Startup Panel - Quick Tab
Optimal Binning Startup Panel - Advanced Tab
Example: Using SIC Codes for Building a Predictive Model
Predictor Screening Introductory Overview
Predictor Screening Startup Panel and Quick Tab
Predictor Screening Startup Panel - Advanced Tab
Predictor Screening Results Dialog Box and Categorical Response Tab
Predictor Screening Results - Continuous Response Tab
Predictor Screening Results - Binary Response Tab
Predictor Screening Results - Save Tab
Feature and Method Selection Computational Details
Feature & Method Selection Startup Panel
Feature Selection Analysis
Feature Selection Analysis - Quick Tab
Generalized Feature Selection Analysis - Advanced Tab
Generalized Feature Selection Analysis
Generalized Feature Selection Analysis - Quick Tab
Generalized Feature Selection Analysis - Advanced Tab
Feature & Model Selection; Classification
Feature & Model Selection; Classification - Quick Tab
Feature & Model Selection; Classification - Advanced Tab
Feature & Model Selection; Regression
Feature & Model Selection; Regression - Quick Tab
Feature & Model Selection; Regression - Advanced Tab
Response Optimization Overview
Response Optimization Startup Panel
Quick Tab
Simplex Tab
Grid Tab
Random Tab
Model Exploration Tab
Progress Bar
Response Optimization Example - Regression
Response Optimization Example - Classification
Response Optimization Technical Notes
General Optimization Overview
Statistica General Optimization Startup Panel and Optimization Algorithm Tab
Statistica General Optimization Startup Panel - Simplex Tab
Statistica General Optimization Startup Panel - Grid Tab
Statistica General Optimization Startup Panel - Genetic Algorithm Tab
General Optimization Example
Quality Control Charts Overview
The Architecture of the Quality Control Charts Module
Quality Control Introductory Overview- General Purpose
Quality Control Introductory Overview - General Approach
Quality Control Introductory Overview - Establishing Control Limits
Quality Control Introductory Overview - Common Types of Charts
Quality Control Introductory Overview - Short Run Charts
Quality Control Events
List of Chart Events
Quality Control Introductory Overview - Unequal Sample Sizes
Quality Control Introductory Overview - Control Charts for Variables vs. Charts for Attributes
Quality Control Introductory Overview - Control Chart for Individual Observations
Quality Control Introductory Overview - Out-of-Control Process: Runs Tests
Quality Control Introductory Overview - Operating Characteristic (OC) Curves
Quality Control Introductory Overview - Process Capability Indices
"Six Sigma" Methodology and Statistica
Measurements Related to Product Quality: Custom Alarm Handling and Custom SVB Scripts
Defining Variables for Attribute Charts
Defining Variables for Attribute Charts - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Variables for (C) Attribute Charts
Defining Variables for C (Attribute) Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Sets, Specifications for Charts from Variables
Defining Sets, Specifications for Charts from Variables - X(X-Bar) tab
Defining Sets, Specifications for Charts from Variables - R (MR, S) Tab
Quality Control Charts - Reviewing Attribute Control Charts Results
Attribute Charts Results - Brushing Tab
Attribute Charts Results - Charts Tab
Attribute Charts Options - Stats Tab
Attribute Charts Results - Parts Tab
Attribute Charts - Options
Attribute Charts Options - Layout Tab
Attribute Charts Options - Scaling Tab
Defining Variables for Pareto Charts
Defining Variables for Pareto Charts - Quick Tab
Defining Variables for Pareto Charts - Labels, Causes, Actions Tab
Reviewing Pareto Charts Results
Pareto Charts Results - Quick Tab
Pareto Charts Results - Options
Pareto Chart Options - Layout Tab
Pareto Chart Options - Shop Floor Tab
Defining Variables for X-bar and Variability Chart
Defining Variables for X-bar and Variability Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Variables for X and Moving Range Chart and CUSUM Chart
Defining Variables for X and Moving Range Chart and CUSUM Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Variables for MA X-bar and R/S Charts
Defining Variables for MA X-bar and R/S Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Variables for EWMA X-bar and R/S Charts
Defining Variables for EWMA X-bar and R/S Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Sets, Specifications for Charts from Variables
Defining Sets, Specifications for Charts from Variables - X(X-Bar) tab
Defining Sets, Specifications for Charts from Variables - R (MR, S) Tab
Reviewing Variable Control Charts, Moving Average Charts, and X & Moving Range Charts Results
Results - R/S-specs Tab
Results - Brushing Tab
Results - Charts Tab
Results - Non-Normal Tab
Results - Parts Tab
Results - Report Tab
Results - Sets Tab
Results - X-Specs Tab
Results - Options
Options - Alarm Tab
Options - Labeling Tab
Options - Layout Tab
Options - Patterns Tab
Options - Scaling Tab
Options - Shop Floor Tab
Options - Stats Tab
Quality Control Charts Startup Panel
Quality Control Charts Startup Panel - Quick Tab
Quality Control Charts Startup Panel - Variables Tab
Quality Control Charts Startup Panel - Attributes Tab
Quality Control Charts Startup Panel - Real-Time Tab
Add Variables
Assigning a Cause, Action, or Comment
Brushing Commands
Brushing Commands
Causes, Actions, Comments, Data Brushing Setup
Chart Center Line
Control Limit Dialogs
Define Actions Resulting From
Edit Text Label
Locking Analysis for Shop Floor
Runs Tests for Control Chart
Sample
Select Specifications to be Saved
Select the Contents and Layout for the Six Graph Summary
Sigma for
Sigma Multiplier for Detecting Shift
Specifications for Capability Analysis
Specify Program (Command Line) and Optional Parameters
Specify Target Values
Specify Text Labels For
Specify Type of Moving Average
Warning Lines
Label (Name) for the New Set of Samples
Compute the Set from Ranges or Codes
Specify the Range of Samples for Computing the Set Statistics
Specify the Coding Variable and Code for the Computations
Apply the Set Statistics and Specifications to Ranges or . . .
Specify the Application Range for the Set
Quality Control Events
Example 1: A Simple Custom Chart Event Handler
Example 2: Custom Response to Specific Alarms
Example 3: Changing the Chart Specifications from Inside Event Handlers
Example 4: Computing Other Statistics or Graphs for Each Chart Update
List of Chart Events
Measurements Related to Product Quality: Custom Alarm Handling and Custom SVB Scripts
QC Charts Example 1: Specifying Control Charts for Variables, Process Capability
QC Charts Example 2: Brushing, Assigning Causes and Actions
QC Charts Example 3: Computing Chart Parameters for Different Sets
QC Charts Example 4: Setting Up Shop Floor Mode and Alarm Notification
QC Charts Example 5: Specifying Control Charts for Attributes, Custom Control Limits
QC Charts Example 6: Specifying Pareto Charts, Changing the Project Header, Printing Results
QC Charts Example 7: Specifying Moving Average/Moving Range charts
QC Charts Example 8: Specifying Charts for Individual Observations and Moving Ranges
QC Charts Example 9: Using IQC on the Shop Floor
QC Charts Example 10: Specifying Control Charts for Short Production Runs
QC Charts Example 11: Specifying CuSum Charts
The Architecture of the Quality Control Charts Module
The Nature of Sets
Auto-Updating of Statistics Displayed in Charts and Spreadsheets
Quality Control Charts - Computational Details
Evaluation of D2(N), D3(N), C4(N), and Related Statistics
Short Run Transformations
Multivariate Quality Control Charts Overview
The Architecture of the Multivariate Quality Control Charts Module
General Purpose
General Approach
Multivariate Quality Control Computational Details
Establishing Control Limits
Common Types of Multivariate Control Charts
Multivariate Quality Control Charts Events
Defining Variables for Hotelling T-Square (Individuals) Chart
Defining Variables for Hotelling T-Square (Individuals) Chart - Quick Tab
Defining Variables for Hotelling T-Square (Individuals) Chart - Sets Tab
Defining Variables for Hotelling T-Square (Individuals) Chart - Specs Tab
Defining Variables for Hotelling T-Square (Individuals) Chart - Labels, Causes, and Actions Tab
Defining Variables for Hotelling T-Square (Means)/GV Chart
Defining Variables for Hotelling T-Square (Means)/GV Chart - Quick Tab
Defining Variables for Hotelling T-Square (Means)/GV Chart - Sets Tab
Defining Variables for Hotelling T-Square (Means)/GV Chart - Specs Tab
Defining Variables for Hotelling T-Square (Means)/GV - Labels, Causes, and Actions Tab
Defining Variables for MEWMA (Individuals) Chart
Defining Variables for MEWMA (Individuals) Chart - Quick Tab
Defining Variables for MEWMA (Individuals) Chart - Sets Tab
Defining Variables for MEWMA (Individuals) Chart - Specs Tab
Defining Variables for MEWMA (Individuals) Chart - Labels, Causes, and Actions Tab
Defining Variables for MEWMA (Means) Chart
Defining Variables for MEWMA (Means) Chart - Quick Tab
Defining Variables for MEWMA (Means) Chart - Sets Tab
Defining Variables for MEWMA (Means) Chart - Specs Tab
Defining Variables for MEWMA (Means) Chart - Labels, Causes, and Actions Tab
Defining Variables for Multivariate CUSUM Chart
Defining Variables for Multivariate CUSUM Chart - Quick Tab
Defining Variables for Multivariate CUSUM Chart - Sets Tab
Defining Variables for Multivariate CUSUM Chart - Specs Tab
Defining Variables for Multivariate CUSUM Chart - Labels, Causes, and Actions Tab
Defining Variables for Multiple Stream X and MR Chart
Defining Variables for Multiple Stream X and MR Chart - Quick Tab
Defining Variables for Multiple Stream X and MR Chart - Sets Tab
Defining Variables for Multiple Stream X and MR Chart - Labels, Causes, and Actions Tab
Defining Variables for Multiple Stream X-Bar and R Chart
Defining Variables for Multiple Stream X-Bar and R Chart - Quick Tab
Defining Variables for Multiple Stream X-Bar and R Chart - Sets Tab
Defining Variables for Multiple Stream X-Bar and R Chart - Labels, Causes, and Actions Tab
Defining Variables for Multiple Stream X-Bar and S Chart
Defining Variables for Multiple Stream X-Bar and S Chart - Quick Tab
Defining Variables for Multiple Stream X-Bar and S Chart - Sets Tab
Defining Variables for Multiple Stream X-Bar and S Chart - Labels, Cause, Actions Tab
Hotelling T2, MEWMA, MCUSUM, and Multiple Stream Results Dialog Boxes
Hotelling T2, MEWMA, MCUSUM, and Multiple Stream Results Dialog Boxes - Charts Tab
Hotelling T2, MEWMA, and MCUSUM Results Dialogs - X(Multivariate) Tab; Multiple Stream Results Dialogs - X(XBar) Specs Tab
Hotelling T2 for Means, and Multiple Stream Charts Results Dialogs - GV Specs/R/S Specs Tab
Hotelling T2, MEWMA, MCUSUM, and Multiple Stream Results Dialog Boxes - Sets Tab
Hotelling T2, MEWMA, MCUSUM, and Multiple Stream Results Dialog Boxes - Brushing Tab
Multivariate Quality Control Charts Startup Panel
Multivariate Quality Control Charts Startup Panel - Quick Tab
Multivariate Quality Control Charts Startup Panel - Real-Time Tab
Options
Options Dialog - Alarms Tab
Options Dialog - Labeling Tab
Options Dialog - Layout Tab
Options Dialog - Patterns Tab
Options Dialog - Scaling Tab
MQC Charts Example 1 - Creating a Hotelling's T2 Chart
MQC Charts Example 2 - Creating a MEWMA Chart for Individuals
MQC Charts Example 3 - Computing Chart Parameters for Different Sets
MQC Charts Example 4 - Creating a Multiple Stream Chart
MQC Charts Example 5 - Creating a Multivariate CUSUM Chart
The Architecture of the Multivariate Quality Control Charts Module
Multivariate Quality Control Computational Details
Quality Control Charts for Variable Lists Startup Panel
Quality Control Charts for Variable Lists - Quick Tab
Quality Control Charts for Variable Lists - Variables Tab
Quality Control Charts for Variable Lists - Attributes Tab
Quality Control Charts for Variable Lists - MSP Tab
Defining Variables for . . .
Defining Variables for Attribute Charts - Quick Tab
Defining Variables for . . .  - Quick Tab (n per sample = 1)
Defining Variables for . . .  - Quick Tab (n per sample > 1)
Pareto Chart
Pareto Chart Dialog - Quick Tab
Variable Control Chart Results
Variable Control Chart Results - X-bar Tab
Variable Control Chart Results - R/S Tab
Variable Control Chart Results - Process Capability Tab
Variable Control Chart Results - Display Tab
Predictive Quality Control Charts Startup Panel
Predictive Quality Control Charts Startup Panel - Quick Tab
Predictive Quality Control Charts Startup Panel - Variables Tab
Predictive Quality Control Charts Startup Panel - Attributes Tab
Predictive Quality Control Charts for Attributes Results
Predictive Quality Control Charts for Attributes Results - Prediction Tab
Predictive Quality Control Charts for Variables Results
Predictive Quality Control Charts for Variables Results - Prediction Tab
Random Forests Overview
Random Forest Startup Panel and Quick Tab
Random Forest Specifications
Random Forest Specifications - Quick Tab
Random Forest Specifications - Advanced Tab
Random Forest Specifications - Stopping Condition Tab
Random Forest Specifications - Classification Tab
Random Forest Results
Random Forest Results - Quick Tab
Random Forest Results - Classification Tab
Random Forest Results - Prediction Tab
Random Forests Results - Report Tab
Example: Classification Random Forests
Example: Regression Random Forests
Rapid Deployment of Predictive Models Overview
Rapid Deployment of Predictive Models
Rapid Deployment of Predictive Models - Quick Tab
Rapid Deployment of Predictive Models - Lift Chart Tab
Rapid Deployment of Predictive Models - Save Results Tab
Rapid Deployment of Predictive Models - Profit Chart/ROC Curve Tab
Rapid Deployment of Predictive Models - Confusion Matrix Tab
Example 1: Creating Deployment Code for Scoring New Records
Example 2: Rapid Deployment of Predictive Models
Stepwise Model Builder Overview
Stepwise Model Builder - Logistic Regression
Marginal Statistics for Stepwise Model Builder - Logistic Regression
Model Statistics for Stepwise Model Builder - Logistic Regression
Stepwise Model Builder Example
Text Mining Overview
Program Overview
Issues and Considerations for Retrieving HTM/HTML Web Pages (Web Crawling)
Web Crawling Document Retrieval Overview
Web Crawling Document Retrieval Dialog
Browsing for Folder Dialog
Text Mining Dialog
Quick Tab
Advanced Tab
Text Mining Words Tab
Project Tab
Filters Tab
Characters Tab
Delimiters Tab
Defaults Tab
Open Document Files
Modification of Synonyms
Word Editor
Assigning Statistics to Variables
Text Mining Results
Summary Tab
Concept Extraction Tab
Search Tab
Save Results Tab
Automatic Classification of Text
Refining Automatic Text Classification with Synonyms and Phrases
Singular Value Decomposition in Statistica Text Mining and Document Retrieval
Weight of Evidence (WoE) Overview
Weight of Evidence (WoE)
WoE Settings
Interaction Terms
Custom Split Groups
Customize Groups for a Categorical Variable
Weight of Evidence (WoE) Technical Notes
Weight of Evidence (WoE) example
Statistics
Basic Statistics Overview
Descriptive Statistics Overview
True Mean and Confidence Level
Shape of the Distribution, Normality
Other Descriptive Statistics
Correlations - Introductory Overview
Simple Linear Correlation (Pearson r)
How to Interpret the Value of Correlation
Significance of Correlations
Outliers
Quantitative Approach to Outliers
Correlations in Non-homogeneous Groups
Nonlinear Relations between Variables
Measuring Nonlinear Relations
Exploratory Examination of Correlation Matrices
Casewise vs. Pairwise Deletion of Missing Data
How to Identify Biases Caused by the Bias due to Pairwise Deletion of Missing Data
Pairwise Deletion of Missing Data vs. Mean Substitution
Spurious Correlation
Are Correlation Coefficients Additive?
How to Determine Whether Two Correlation Coefficients are Significant
Other Correlation Coefficients
t-test for Independent Samples - Introductory Overview
t-test for Independent Samples - Arrangement of Data
t-test for Independent Samples - Alternative Arrangement of Data
t-test for Independent Samples - t-test Graphs
t-test for Independent Samples - More Complex Group Comparisons
t-test for Dependent Samples - Within-Group Variation
t-Test for dependent samples - purpose
t-test for Dependent Samples - Assumptions
t-test for Dependent Samples - Arrangement of Data
t-test for Dependent Samples - Matrices of t-tests
t-test for Dependent Samples - More Complex Group Comparisons
t-test for Single Means - Introductory Overview
Descriptive Statistics by Groups (Breakdown) - Introductory Overview
Arrangement of Data
Statistical Tests in Breakdowns
Other Related Data Analysis Techniques
Post-hoc Comparisons of Means
Breakdown vs. Discriminant Function Analysis
Breakdown vs. Frequency Tables
Graphical Breakdowns
Frequency Tables Overview
Description of Joint Frequency Distributions with Crosstabulations
2 x 2 Tables
Marginal Frequencies
Column, Row, and Total Percentages
Graphical Representations of Crosstabulations
Stub-and-Banner Tables
Multiway Tables with Control Variables
Graphical Representations of Multi-way Tables
Statistics in Crosstabulations
Pearson Chi-square
Maximum Likelihood Chi-square
Yates Correction
Fisher Exact Test
McNemar Chi-square
Coefficient Phi
Coefficient of Contingency
Cramer's V
Tetrachoric Correlation
Interpretation of Contingency Measures
Statistics in Crosstabulations - Statistics Based on Ranks
Multiple Responses/Dichotomies - Overview
Multiple Responses/Dichotomies - Multiple Response Variables
Multiple Responses/Dichotomies - Multiple Dichotomies
Multiple Responses/Dichotomies - Crosstabulation of Multiple Responses/Dichotomies
Multiple Responses/Dichotomies - Paired Crosstabulation of Multiple Response Variables
Multiple Responses/Dichotomies - A Final Comment
Probability Distribution Calculator - Overview
Beta Distribution for the Probability Distribution Calculator
Cauchy Distribution for the Probability Distribution Calculator
Chi-square Distribution for the Probability Distribution Calculator
Exponential Distribution for the Probability Distribution Calculator
Extreme Value Distribution for the Probability Distribution Calculator
F Distribution for the Probability Distribution Calculator
Gamma Distribution for the Probability Distribution Calculator
Laplace Distribution for the Probability Distribution Calculator
Logistic Distribution for the Probability Distribution Calculator
Log-normal Distribution for the Probability Distribution Calculator
Pareto Distribution for the Probability Distribution Calculator
Rayleigh Distribution for the Probability Distribution Calculator
t (Student) Distribution for the Probability Distribution Calculator
Weibull Distribution for the Probability Distribution Calculator
Z (Normal) Distribution for the Probability Distribution Calculator
Other Significance Tests - Introductory Overview
Basic Statistics and Tables
Basic Statistics and Tables - Quick Tab
Probability Distribution Calculator
Pearson Product Moment Correlation Distribution
Difference Tests: R, %, Means
Basic Statistics and Tables dialog box - Quick tab
Descriptive Statistics - Quick Tab
Descriptive Statistics - Advanced tab
Descriptive Statistics - Robust Tab
Descriptive Statistics - Normality Tab
Descriptive Statistics - Prob. & Scatterplots tab
Descriptive Statistics - Categ. Plots tab
Descriptive Statistics - Options tab
Product-Moment and Partial Correlations
Product-Moment and Partial Correlations - Quick tab
Product-Moment and Partial Correlations - Advanced Tab
Product-Moment and Partial Correlations - Options tab
T-Test for Independent Samples by Groups
T-Test for Independent Samples by Groups - Quick Tab
T-Test for Independent Samples by Groups - Advanced Tab
T-Test for Independent Samples by Groups - Options Tab
T-Test for Independent Samples by Variables
T-Test for Independent Samples by Variables - Quick Tab
T-Test for Independent Samples by Variables - Options Tab
T-Test for Dependent Samples
T-Test for Dependent Samples - Quick tab
T-Test for Dependent Samples - Advanced tab
T-Test for Single Means
T-Test for Single Means - Quick tab
T-Test for Single Means - Advanced tab
T-Test for Single Means - Options tab
Select Reference Values
Statistics by Groups (Breakdown)
Statistics by Groups (Breakdown) - Lists of Tables tab
Statistics by Groups (Breakdown) - Individual Tables tab
Select Codes for ...
Statistics by Groups Results
Statistics by Groups Results - Quick tab
Statistics by Groups Results - Post-Hoc tab
Statistics by Groups Results - Descriptives tab
Order of Factors for Breakdown Tables
Specific Marginal Table
Statistics by Groups Results - ANOVA & Tests tab
Select the Variables for Interaction Plot
Arrangement of Factors
Statistics by Groups Results - Correlations tab
Order of Factors for Breakdown Tables
Select Groups
Statistics Breakdown (Non-Factorial)
Statistics Breakdown (Non-Factorial) - Quick tab
Statistics Breakdown (Non-Factorial) - Descriptives tab
Descriptive Statistics - Robust Tab
Frequency Tables
Frequency Tables - Quick Tab
Frequency Tables - Options Tab
Frequency Tables - Descr. Tab
Frequency Tables - Normality Tab
Frequency Tables - Advanced Tab
Select Codes for ...
Define Categories
Crosstabulation Tables
Crosstabulation Tables - Stub-and-Banner Tab
Crosstabulation Tables - Crosstabulation Tab
Select the Tables to be Deleted
Select Codes for ...
Crosstabulation Tables Results
Crosstabulation Tables Results - Quick Tab
Crosstabulation Tables Results - Options Tab
Select the Tables You Want to Review
Crosstabulation Tables Results - Advanced Tab
Select the Variables for Interaction Plot
Arrangement of Factors
Multiple Response Tables
Multiple Response Tables - Quick Tab
Multiple Response Tables - Options Tab
Long Factor Labels for Multiple Response Sets
Multiple Response Table Results
Multiple Response Table Results - Quick Tab
Multiple Response Table Results - Options Tab
Multiple Response Table Results - Advanced Tab
Select the Variables for Interaction Plot
Arrangement of Factors
Example 1: Descriptive Statistics, t-Tests, and Correlations
Example 2: Breakdown and One-Way ANOVA
Example 3: Frequency Tables
Example 4: Stub-and-Banner Tables
Example 5: Crosstabulation Tables
Example 6: Tabulating Multiple Responses and Dichotomies
Example 7: Detecting Outliers
Example 8: Visually Comparing Variables
Example 9: Using Contingency Tables to Compute Chi-Square Tests for Independence
Example 10: Chi-Square Test for Independence
Selecting a Weighting Variable
A Note on Precision
Statistics in Breakdown Procedures
Statistica Reporting Tables Overview
Statistica Reporting Tables
Code Settings
Multiple Response
Crosstable Group
Parameters Settings
Output Parameters Settings
Global Settings
Layout Style Selection
Add/Edit
Multiple Regression Analysis Overview
Multiple Regression Analysis Introductory Overview - Computational Approach
Computational Approach - Least Squares
Computational Approach - Weighted Least Squares
Computational Approach - The Regression Equation
Computational Approach - Unique Prediction and Partial Correlation
Computational Approach - Predicted and Residual Scores
Computational Approach - Residual Variance and R-square
Computational Approach - Interpreting the Correlation Coefficient R
Assumptions, Limitations, Practical Considerations - Assumption of Linearity
Assumptions, Limitations, Practical Considerations - Normality Assumption
Assumptions, Limitations, Practical Considerations - Limitations
Assumptions, Limitations, Practical Considerations - Choice of the Number of Variables
Assumptions, Limitations, Practical Considerations - Multicollinearity and Matrix Ill-conditioning
Assumptions, Limitations, Practical Considerations - The Importance of Residual Analysis
Multiple Linear Regression Startup Panel
Multiple Linear Regression Startup Panel - Quick Tab
Multiple Linear Regression Startup Panel - Advanced Tab
Model Definition
Model Definition - Quick Tab
Model Definition - Advanced Tab
Model Definition - Descriptives Tab
Model Definition - Stepwise Tab
Multiple Regression Results
Multiple Regression Results - Quick Tab
Multiple Regression Results - Advanced Tab
Multiple Regression Results - Residuals/Assumptions/Prediction Tab
Specify Values for Indep. Vars
Residual Analysis
Residual Analysis - Advanced Tab
Residual Analysis - Residuals Tab
Residual Analysis - Predicted Tab
Residual Analysis - Scatterplots Tab
Residual Analysis - Probability Plots Tab
Residual Analysis - Outliers Tab
Residual Analysis - Save Tab
Residual Analysis - Quick Tab
Residuals and Predicted Values
Review Descriptive Statistics
Review Descriptive Statistics - Quick Tab
Review Descriptive Statistics - Advanced Tab
Review Descriptive Statistics - Matrix Tab
Example 1: Standard Regression Analysis
Example 2: Stepwise Regression Analysis
Multiple Regression - Notes and Technical Information
General ANOVA/MANOVA Overview
ANOVA/MANOVA - Methods for Specifying Designs
General ANOVA/MANOVA
General ANOVA/MANOVA - Quick Tab
Quick Tab
One-way ANOVA in General ANOVA/MANOVA
Main Effects ANOVA in General ANOVA/MANOVA
Factorial ANOVA in General ANOVA/MANOVA
Repeated Measures ANOVA in General ANOVA/MANOVA
ANOVA/MANOVA Quick Specs
ANOVA/MANOVA Quick Specs - Options Tab
ANOVA/MANOVA Quick Specs - Quick Tab
Specify Within-Subjects Factor
Select Codes for ...
MAN Analysis Wizard--Between Design
MAN Analysis Wizard--Between Design - Default Between Design Tab
MAN Analysis--Wizard Between Design - Custom Between Design Tab
MAN Analysis Wizard--Extended Options
MAN Analysis Wizard--Extended Options - Quick Tab
MAN Analysis Wizard--Extended Options - Advanced Tab
ANOVA/ MANOVA Syntax - Overview
MAN Analysis Syntax Editor
MAN Analysis Syntax Editor - Keywords Tab
MAN Analysis Syntax Editor - Specifications Tab
ANOVA Results
ANOVA Results - Quick Tab
GLM and ANOVA Results - Summary Tab
GLM, GRM, and ANOVA Results - Means Tab
GLM, GRM, and ANOVA Results - Comps Tab
GLM, GRM, and ANOVA Results - Resids Tab
GLM, GRM, and ANOVA Results - Matrix Tab
GLM, GLZ, GRM, PLS, and ANOVA Results - Report Tab
GLM, GRM, and ANOVA Results - Profiler Tab
Desirability Profiling in GLM, GRM, and MANOVA
ANOVA More Results
GLM and ANOVA More Results - Summary Tab
GLM, GRM, and ANOVA More Results - Means Tab
GLM, GRM, and ANOVA More Results - Planned Comps Tab
GLM, GRM, and ANOVA More Results - Post-hoc Tab
GLM, GRM, and ANOVA More Results - Assumptions Tab
GLM, GRM, and ANOVA More Results - Custom Tests Tab
GLM, GRM, and ANOVA More Results - Residuals 1 Tab
GLM, GRM, and ANOVA More Results - Residuals 2 Tab
GLM, GRM, and ANOVA More Results - Matrix Tab
GLM, GRM, and ANOVA More Results - Report Tab
GLM, GRM, and ANOVA More Results - Profiler Tab
Desirability Profiling in GLM, GRM, and MANOVA
Example 1: Simple Factorial ANOVA with Repeated Measures
Example 2: A 2 x 3 Between-Groups ANOVA Design
Example 3: A 2-Level Between-Group x 4-Level Within-Subject Repeated Measures Design
ANOVA/MANOVA Syntax - Examples
ANOVA/MANOVA - Notes and Technical Information
ANOVA/MANOVA - Methods for Analysis of Variance
Nonparametric Statistics Introductory Overview - General Purpose
Nonparametric Methods Overview
Nonparametric Statistics Introductory Overview - When to Use Which Method
Nonparametric Correlations
Nonparametric Statistics Startup Panel and Quick Tab
2 x 2 Tables
2x2 Tables - Quick Tab
Observed vs. Expected Frequency
Observed vs. Expected Frequency - Quick Tab
Nonparametric Correlations
Nonparametric Correlation - Quick Tab
Nonparametric Correlation - Advanced Tab
Comparing Two Groups
Comparing Two Groups - Quick Tab
Kruskal-Wallis ANOVA and Median Test
Kruskal-Wallis ANOVA and Median Test - Quick Tab
Comparing Two Variables
Comparing Two Variables - Quick Tab
Friedman ANOVA by Ranks
Friedman ANOVA by Ranks - Quick Tab
Box-Whisker Type
Cochran Q Test
Cochran Q Test - Quick Tab
Descriptive Statistics
Descriptive Statistics - Quick Tab
Box-Whisker Type
Example 1: 2 x 2 Tables
Example 2: Sign Test
Example 3: Observed vs. Expected Chi-Square
Example 4: Correlations
Example 5: Wald-Wolfowitz Runs Test, Mann-Whitney U Test, Kolmogorov-Smirnov Two-Sample Test
Example 6: Kruskal-Wallis ANOVA & Median Test
Example 7: Friedman ANOVA & Kendall Concordance
Example 8: Wilcoxon Matched Pairs Test
Example 9: Cochran Q Test
Example 10: Descriptive Statistics
Nonparametric Statistics Notes - 2x2 Tables, Chi/V/Phi Square, McNemar, Fisher Exact
Nonparametrics Statistics Notes - Observed vs. Expected Chi-square
Nonparametrics Statistics Notes - Correlations (Spearman, Kendall tau, Gamma)
Nonparametric Statistics Notes - Wald-Wolfowitz Runs Test
Nonparametrics Statistics Notes - Kolmogorov-Smirnov Two-Sample Test
Nonparametrics Statistics Notes - Mann-Whitney U Test
Nonparametrics Statistics Notes - Kruskal-Wallis ANOVA by Ranks and Median Test
Nonparametrics Statistics Notes - Sign Test
Nonparametrics Statistics Notes - Wilcoxon Matched Pairs Test
Nonparametric Statistics Notes - Friedman ANOVA and Kendall Concordance
Nonparametric Statistics Notes - Cochran Q Test
Nonparametric Statistics Notes - Ordinal Descriptive Statistics
Distribution Fitting Overview
Distribution Fitting Introductory Overview - Fit of the Distribution
Distribution Fitting Introductory Overview - Types of Distributions
Distribution Fitting Startup Panel and Quick Tab
Fitting Continuous Distributions
Fitting Continuous Distributions - Quick Tab
Fitting Continuous Distributions - Parameters Tab
Fitting Continuous Distributions - Options Tab
Fitting Discrete Distributions
Fitting Discrete Distributions - Quick Tab
Fitting Discrete Distributions - Parameters Tab
Fitting Discrete Distributions - Options Tab
Distribution Fitting Example
Distributions & Simulation Overview
Distributions & Simulation Analysis Startup Panel and Quick Tab
Fit Distributions Dialog Box and Quick Tab
Fit Distributions - Continuous Variables Tab
Fit Distributions - Discrete Variables Tab
Fit Distributions - Options Tab
Simulation Methods
Design Simulation
Define Distributions
Default Distributions
Correlation Parameters
Model Profiler Introductory Overview
Model Profiler
Model Profiler - Profiler Tab
Model Profiler - Output Tab
Example: Model Profiler in Distributions & Simulation
Example: Model Profiler in DOE
Example: Model Profiler in GLM
Fit Distributions Results Dialog Box and Quick Tab
Save Fit
Define Distributions
Distributions & Simulation Example
Advanced Linear Nonlinear Models
Generalized Linear Model (GLM) Overview
Generalized Linear Model (GLM) Overview - Basic Ideas: The General Linear Model
Generalized Linear Model (GLM) Overview - Historical Background
Generalized Linear Model (GLM) Overview - The Purpose of Multiple Regression
Generalized Linear Model (GLM) Overview - Computations for Solving the Multiple Regression Equation
Generalized Linear Model (GLM) Introductory Overview - Extension of Multiple Regression to the General Linear Model
Generalized Linear Model (GLM) Introductory Overview - Sigma-Restricted and Overparameterized Model
Generalized Linear Model (GLM) Introductory Overview - Summary of Computations
Generalized Linear Model (GLM) Introductory Overview - Types of Analyses
Generalized Linear Model (GLM) Introductory Overview - Multivariate Designs
Generalized Linear Model (GLM) Introductory Overview - Between-Subject Designs Overview
GLM Introductory Overview - One-Way ANOVA
GLM Introductory Overview - Main Effect ANOVA
GLM Introductory Overview - Factorial ANOVA
GLM Introductory Overview - Nested ANOVA Designs
GLM Introductory Overview - Balanced ANOVA
GLM Introductory Overview - Simple Regression
GLM Introductory Overview - Multiple Regression
GLM Introductory Overview - Factorial Regression
GLM Introductory Overview - Polynomial Regression
GLM Introductory Overview - Response Surface Regression
GLM Introductory Overview - Mixture Surface Regression
Analysis of Covariance (ANCOVA) - Basic Ideas
GLM Introductory Overview - Analysis of Covariance
GLM Introductory Overview - Separate Slope Designs
GLM Introductory Overview - Homogeneity of Slopes
GLM Introductory Overview - Mixed Model ANOVA and ANCOVA
Generalized Linear Model (GLM) Introductory Overview - Within-Subject (Repeated Measures) Designs Overview
GLM Introductory Overview - One-Way Within-Subject Designs
GLM Introductory Overview - Multi-Way Within-Subject Designs
GLM Introductory Overview - Multivariate Approach to Repeated Measures
GLM Introductory Overview - Doubly Multivariate Designs
Generalized Linear Model (GLM) Introductory Overview - Hypothesis Testing
Generalized Linear Model (GLM) Hypothesis Testing - Testing the Whole Model
GLM Hypothesis Testing - Partitioning Sums of Squares
GLM Hypothesis Testing - Limitations of Whole Model Tests
Generalized Linear Model (GLM) Hypothesis Testing - Six Types of Sums of Squares
GLM Hypothesis Testing - Contained Effects
GLM Hypothesis Testing - Type I Sums of Squares
GLM Hypothesis Testing - Type II Sums of Squares
GLM Hypothesis Testing - Type III Sums of Squares
GLM Hypothesis Testing - Type IV Sums of Squares
GLM Hypothesis Testing - Type V Sums of Squares
GLM Hypothesis Testing - Type VI (Effective Hypothesis) Sums of Squares
Generalized Linear Model (GLM) Hypothesis Testing - Repeated Measures and Multiple Dependent Variables
Generalized Linear Model (GLM) Hypothesis Testing - Testing Specific Hypotheses
GLM Hypothesis Testing - Estimability of Hypotheses
GLM Hypothesis Testing - Linear Combinations of Effects
GLM Hypothesis Testing - Planned Comparisons of Least Square Means
GLM Hypothesis Testing - Post-Hoc Comparisons
A-Priori Comparisons of Least Observed Squares Means vs. Post-hoc Comparisons of Means
Generalized Linear Model (GLM) Hypothesis Testing - Lack-of-Fit Tests using Pure Error
GLM Hypothesis Testing - Designs with Zero Degrees of Freedom for Error
GLM Hypothesis Testing - Tests in Mixed Model Designs
GLM Introductory Overview - Comparison with Other General Linear Model Programs
Generalized Linear Model (GLM) Introductory Overview - Sigma-Restricted and Overparameterized Model
Generalized Linear Model (GLM) Unique Features - Efficient Computations for Balanced ANOVA Designs
GLM Unique Features - Analysis of Incomplete Designs
GLM Unique Features - Planned Comparisons of Least Squares Means
Generalized Linear Model (GLM) Unique Features - Post-Hoc Tests for Repeated Measures Effects
Generalized Linear Model (GLM) Unique Features - Plots of Interactions
Generalized Linear Model (GLM) Unique Features - Desirability Profiles and Response Optimization
Generalized Linear Model (GLM) Unique Features - Tests of Assumptions, Residual Statistics
Generalized Linear Model (GLM) Unique Features - Cross-Validation and Prediction Samples
One-way ANOVA in GLM, GLZ, and PLS
Main Effects ANOVA in GLM, GLZ, and PLS
Factorial ANOVA in GLM, GLZ, and PLS
Nested Design ANOVA in GLM, GLZ, and PLS
Huge Balanced ANOVA in GLM and GLZ
Repeated Measures ANOVA in GLM and GLZ
Simple Regression in GLM, GLZ, and PLS
Multiple Regression in GLM, GLZ, and PLS
Factorial Regression in GLM, GLZ, and PLS
Polynomial Regression in GLM, GLZ, and PLS
Response Surface Regression in GLM, GLZ, and PLS
Mixture Surface Regression in GLM and GLZ
Analysis of Covariance in GLM, GLZ, and PLS
Separate-Slopes Model in GLM, GLZ, and PLS
Homogeneity-of-Slopes Model in GLM, GLZ, and PLS
General Linear Models in GLM, GLZ, and PLS
GLM - Methods for Specifying Designs
General Linear Models (GLM) Startup Panel
General Linear Models (GLM) and Quick Tab
GLM Analysis Syntax Editor
GLM Analysis Syntax Editor - Keywords Tab
Tibco2020GLM Analysis Syntax Editor - Specifications Tab
Variable Names to Insert
SAS Syntax - Specification Options for SAS Syntax
SAS Syntax - Overview
SAS Syntax - Keyword Class
SAS Syntax - Keyword Model
SAS Syntax - Keyword Random
SAS Syntax - Keyword Repeated
GLM (Statistica) Syntax - Overview
GLM, GLZ, GRM, PLS, and GDA Syntax - Keyword Covariate
GLM, GRM, PLS, and MANOVA Syntax - Keyword Dependent
GLM, GLZ, PLS, GDA, and MANOVA Syntax - Keyword Design
GLM, GRM, and MANOVA Syntax - Keyword Estimate
GLM, GLZ, GRM, PLS, GDA, and MANOVA Syntax - Keyword Groups
GLM, GLZ, GRM, GDA, and MANOVA Syntax - Keyword IDelta
GLM, GLZ, GRM, and MANOVA Syntax - Keyword Intercept
GLM, GRM, and MANOVA Syntax - Keyword Lackoffit
GLM, GLZ, GRM, and PLS Syntax - Keyword Mixture
GLM, GLZ, GRM, PLS, GDA, MANOVA, and SAS Syntax - Keyword Output
GLM, GLZ, PLS, and MANOVA Syntax - Keyword Param
GLM Syntax - Keyword Random
GLM and MANOVA Syntax - Keyword Repeated
GLM, GLZ, GRM, PLS, GDA, and MANOVA Syntax - Keyword Sample
GLM, GLZ, GRM, GDA, and MANOVA Syntax - Keyword SDelta
GLM and MANOVA Syntax - Keyword SSType 
GLM, GLZ, GRM, PLS, and GDA Syntax - Keyword Surface
GLM and MANOVA Syntax - Keyword WDesign
Alternative Method for Specifying GLM Designs in Macros (Automation)
GLM Analysis Wizard Between Design
GLM Analysis Wizard Between Design - Default Between Design Tab
GLM Analysis Wizard Between Design - Custom Between Design Tab
GLM Analysis Wizard Extended Options
GLM Analysis Wizard Extended Options - Quick Tab
GLM Analysis Wizard Extended Options - Advanced Tab
GLM Quick Specs
GLM Quick Specs - Quick Tab
GLM Quick Specs - Options Tab
GLM Between Effects (Factorial Design)
Nesting For Between-Groups Factors
GLM Between Effects (Polynomial Regression)
Random Effects (Mixed Model)
GLM Between Effects
GLM, GLZ, GRM, and PLS - Specifying Effects for the Between Group Design
Specify Within-Subjects Factors
GLM Effects for Within Design
GLM Specifying Repeated Measures Designs - Specifying More than One Repeated Measures Factor
GLM Specifying Repeated Measures Designs - Multivariate Repeated Measures Designs
GLM Results
GLM Results - Quick Tab
Table of All Effects
GLM and ANOVA Results - Summary Tab
Specify Effect to Estimate
Table of All Effects
Summary Results for Between Effects in GLM and ANOVA
Summary Results for Within Effects in GLM and ANOVA
Summary Results for Random Effects in GLM
GLM, GRM, and ANOVA Results - Means Tab
Values for Covariates
Specify the Arrangement of the Factors in the Plot
Dependent Vars for the Plot
GLM, GRM, and ANOVA Results - Comps Tab
Specify Contrasts / Repeated Measures Factors
Specify Contrasts for this Factor / Enter Contrasts for this Factor / Contrasts for Dependent Variables
Contrasts for Between-Group Factors / Contrasts for Within-Subject Factors
Polynomial Coefficients
Predefined Contrasts
GLM, GRM, and ANOVA Results - Profiler Tab
Desirability Profiling in GLM, GRM, and MANOVA
Options for Response Profiler
Specifications for Factor Grid
Select Factor/Covariate Values
GLM, GRM, and ANOVA Results - Resids Tab
GLM, GRM, and ANOVA Results - Matrix Tab
Between Design Matrices in GLM, GRM, and ANOVA
Between Effects Matrices in GLM, GRM, and ANOVA
Within Effects Matrices in GLM, GRM, and ANOVA
GLM, GLZ, GRM, PLS, and ANOVA Results - Report Tab
GLM  More Results
GLM and ANOVA More Results - Summary Tab
Summary More Results for Between Effects in GLM, GRM, and ANOVA
Summary More Results for Within Effects in GLM and ANOVA
GLM, GRM, and ANOVA More Results - Means Tab
GLM, GRM, and ANOVA More Results - Planned Comps Tab
GLM, GRM, and ANOVA More Results - Post-hoc Tab
Post-hoc Tests in GLM, GRM, and ANOVA
Display Post-hoc Tests Results in GLM, GRM, and ANOVA
Error Term for Post-hoc Tests in GLM, GRM, and ANOVA
Variables for Post Hoc Tests
GLM, GRM, and ANOVA More Results - Assumptions Tab
Select Groups
GLM, GRM, and ANOVA More Results - Profiler Tab
Desirability Profiling in GLM, GRM, and MANOVA
GLM, GRM, and ANOVA More Results - Custom Tests Tab
GLM, GRM, and ANOVA More Results - Residuals 1 Tab
GLM, GRM, and ANOVA More Results - Residuals 2 Tab
GLM, GRM, and ANOVA More Results - Matrix Tab
GLM, GRM, and ANOVA More Results - Report Tab
Example 1: A 2 x 3 Between-Groups Factorial ANOVA Design
Example 2: One-Way ANOVA Design
Example 3: Main Effects ANOVA Design
Example 4: Mixed-Model Factorial ANOVA Design
Example 5: Mixed-Model Nested ANOVA Design
Example 6: Repeated Measures ANOVA Design
Example 7: Simple Regression Analysis
Example 8: Multiple Regression Analysis
Example 9: Response Surface Design
Example 10: Mixture Surface Design
Example 11: MANCOVA Design
Advanced Example 1: Type V Sums of Squares
Advanced Example 2: Planned Comparisons of Least Squares Means
Advanced Example 3: Custom Within Designs
Advanced Example 4: Post-Hoc Tests for Repeated Measures
Advanced Example 5: Specifying a Process-Mixture Design
Advanced Example 6: Response Optimization
Advanced Example 7: Cross-Validation
GLM Syntax Examples Overview
GLM Syntax - Example 1: 4-Way Between Group Design
GLM Syntax - Example 2: Between-within (repeated measures) design
GLM Syntax - Example 3: Hierarchically Nested Design with Random Effects
GLM Syntax - Example 4: Multiple Regression
GLM Syntax - Example 5: 2(k-p) Fractional Factorial
GLM Syntax - Example 6: Response Surface Design and Lack of Fit
GLM Syntax - Example 7: Mixture Design
GLM Syntax - Example 8: Mixture/Process Design
GLM Syntax - Example 9: Multivariate Repeated Measures
GLM Syntax - Example 10: Equality of Regression Coefficients
GLM Syntax - Example 11: Cross-over design
General ANOVA/MANOVA and GLM - Notes
General ANOVA/MANOVA and GLM Notes - Specifying Univariate and Multivariate Between-Groups Designs
Specifying Univariate and Multivariate Between-Groups Designs - One-Way Between-Groups ANOVA and Contrast Analysis
Specifying Univariate and Multivariate Between-Groups Designs - Multi-Way Between-Groups ANOVA and Contrast Analysis
Specifying Univariate and Multivariate Between-Groups Designs - Isolated ("Hanging") Control Group
General ANOVA/MANOVA and GLM Notes - Specifying Within-Subjects (Repeated Measures) Univariate and Multivariate Designs
Specifying Within-Subjects (Repeated Measures) Univariate and Multivariate Designs - One-Way Within-Subjects ANOVA
Specifying Within-Subjects (Repeated Measures) Univariate and Multivariate Designs - One-Way Within-Subjects (Repeated Measures) ANOVA with Multiple Dependent Measures (MANOVA)
Specifying Within-Subjects (Repeated Measures) Univariate and Multivariate Designs - Multi-Way Within-Subjects ANOVA
Specifying Within-Subjects (Repeated Measures) Univariate and Multivariate Designs - Multivariate (MANOVA) Multi-Way Within-Subjects (Repeated Measures) Designs
Specifying Within-Subjects (Repeated Measures) Univariate and Multivariate Designs - Between-Within Univariate (ANOVA) and Multivariate (MANOVA) Designs
General ANOVA/MANOVA and GLM Notes - Multiple Dependent Measures (MANOVA)
General ANOVA/MANOVA and GLM Notes - Nested Designs and Latin Squares
Nested Designs and Latin Squares - Simple Between-Groups Nested Designs
Nested Designs and Latin Squares - Latin Squares, Greco-Latin Squares, Generalized Latin Squares
General ANOVA/MANOVA and GLM Notes - Other Special Designs
Other Special Designs - Designs with Cell N of 1
Other Special Designs - Randomized Block Designs
General ANOVA/MANOVA and GLM - Covariates
General ANOVA/MANOVA and GLM Notes - Random Effects
General ANOVA/MANOVA and GLM Notes - Sums of Squares
Sums of Squares - Multiple Regression and ANOVA
Sums of Squares - Unbalanced and Balanced Designs
Sums of Squares - Type I, II, III, and IV Sums of Squares
Sums of Squares - Interpreting Type I, II, III Hypotheses
Generalized Linear/Nonlinear Models (GLZ) Overview
GLZ Introductory Overview - Basic Ideas
GLZ Introductory Overview - Computational Approach
GLZ Introductory Overview - Types of Analyses
GLZ Introductory Overview - Model Building
GLZ Introductory Overview - Interpretation of Results and Diagnostics
GLZ - Methods for Specifying Designs
Generalized Linear/Nonlinear Models Startup Panel
Generalized Linear/Nonlinear Models - Quick Tab
Generalized Linear/Nonlinear Models - Advanced Tab
Specification Methods in GLZ
GLZ Computing
GLZ Analysis Syntax Editor
GLZ Analysis Syntax Editor - Keywords Tab
GLZ Analysis Syntax Editor - Specifications Tab
GLZ Syntax - Overview
GLZ Syntax - Keyword Converge
GLZ Syntax - Keyword Countv
GLZ Syntax - Keyword Initials
GLZ Syntax - Keyword Maxiter
GLZ Syntax - Keyword Offset
GLZ Syntax - Keyword Response
Best Subset Regression
Stepwise Regression
GLZ Syntax - Keyword Mbuild
Options for MBUILD
Alternative Method for Specifying GLM Designs in Macros (Automation)
GLZ Analysis Wizard Between Design
GLZ Analysis Wizard Between Design - Default Between Design Tab
GLZ Analysis Wizard Between Design - Custom Between Design Tab
GLZ Analysis Wizard Extended Options
GLZ Analysis Wizard Extended Options - Quick Tab
GLZ Analysis Wizard Extended Options - Advanced Tab
Select Start Values
GLZ Quick Specs
GLZ Quick Specs - Quick Tab
GLM Between Effects
GLZ Quick Specs - Advanced Tab
Select Start Values
GLZ Results
GLZ Results - Resid. 1 Tab
GLZ Results - Resid. 2 Tab
GLZ Results - Means Tab
GLM, GLZ, GRM, PLS, and ANOVA Results - Report Tab
GLZ Results - Summary Tab
Model Building Results in GLZ
All Effects Results in GLZ
Sample Options in GLZ
Example 1: Normal Linear Model with Log Link
Example 2: Binomial Linear Model with Logit Link (Logistic Regression)
Example 3: Ordinal Multinomial Linear Model with Probit Link
Example 4: Multinomial Linear Model with Generalized Logit Link
Example 5: Tweedie Distribution with Log Link
GLZ Syntax Examples
GLZ Syntax - Example 1: Multiple Regression with Categorical Predictors
GLZ Syntax - Example 2: Stepwise Multiple Regression
GLZ Syntax - Example 3: Best Subset Regression with Multinomial Response
GLZ Syntax - Example 4: Factorial Design and Binomial Response
Stepwise Model Builder Overview
Stepwise Model Builder - Logistic Regression
Marginal Statistics for Stepwise Model Builder - Logistic Regression
Model Statistics for Stepwise Model Builder - Logistic Regression
Stepwise Model Builder Example
Stepwise Model Builder - Cox Regression Introductory Overview
Stepwise Model Builder - Cox Regression
Marginal Statistics for Stepwise Model Builder - Cox Regression
Model Statistics for Stepwise Model Builder - Cox Regression
Stepwise Model Builder - Linear Regression Introductory Overview
Stepwise Model Builder - Linear Regression
Marginal Statistics for Stepwise Model Builder - Linear Regression
Model Statistics for Stepwise Model Builder - Linear Regression
General Regression Models (GRM) Overview
GRM Introductory Overview - Basic Ideas: The Need for Simple Models
GRM Introductory Overview - Model Building in GRM
GRM Introductory Overview - Types of Analyses
GRM Introductory Overview - Between-Subject Designs Overview
GRM Introductory Overview - Simple Regression
GRM Introductory Overview - Multiple Regression
GRM Introductory Overview - Factorial Regression
GRM Introductory Overview - Polynomial Regression
GRM Introductory Overview - Response Surface Regression
GRM Introductory Overview - Mixture Surface Regression
GRM Introductory Overview - One-Way ANOVA
GRM Introductory Overview - Main Effect ANOVA
GRM Introductory Overview - Factorial ANOVA
GRM Introductory Overview - Analysis of Covariance
GRM Introductory Overview - Homogeneity of Slopes
GRM Introductory Overview - Multivariate Designs Overview
GRM Introductory Overview - Building the Whole Model
GRM Whole Model - Partitioning Sums of Squares
GRM Whole Model - Testing the Whole Model
GRM Whole Model - Limitations of Whole Models
GRM Introductory Overview - Building Models via Best-Subset Regression
GRM Introductory Overview - Building Models via Stepwise Regression
GRM Stepwise Regression - The Initial Model in Stepwise Regression
GRM Introductory Overview - The Forward Entry Method
GRM Introductory Overview - The Backward Removal Method
GRM Introductory Overview - The Forward Stepwise Method
GRM Introductory Overview - The Backward Stepwise Method
GRM Introductory Overview - Entry and Removal Criteria
GRM Introductory Overview - Comparison with Other Regression Programs
GLM Unique Features - Planned Comparisons of Least Squares Means
Generalized Linear Model (GLM) Unique Features - Plots of Interactions
Generalized Linear Model (GLM) Unique Features - Desirability Profiles and Response Optimization
Generalized Linear Model (GLM) Unique Features - Tests of Assumptions, Residual Statistics
Generalized Linear Model (GLM) Unique Features - Cross-Validation and Prediction Samples
GLM Unique Features - Sigma-Restricted and Overparameterized Models
GLM Unique Features - Analysis of Incomplete Designs
Simple Regression in GRM
Multiple Regression in GRM
Factorial Regression in GRM
Polynomial Regression in GRM
Response Surface Regression in GRM
Mixture Surface Regression in GRM
One-way ANOVA Designs in GRM
Main Effects ANOVA in GRM
Factorial ANOVA in GRM
Analysis of Covariance in GRM
Homogeneity-of-Slopes Model in GRM
General Linear Models in GRM
GRM - Methods for Specifying Designs
General Regression Models Startup Panel
General Regression Models Startup Panel and Quick Tab
GRM Analysis Syntax Editor
GRM Analysis Syntax Editor - Keywords Tab
GRM Analysis Syntax Editor - Specifications Tab
GRM Syntax - Overview
GRM Syntax - Examples
GRM Syntax - Keyword Design
GRM and GDA Syntax - Keyword Force
GRM and GDA Syntax - Keyword Mbuild
GRM and GDA Syntax - Stepwise Regression
GRM and GLZ Syntax - Best Subset Regression
GRM and GDA Syntax - Options for MBUILD
Alternative Method for Specifying GLM Designs in Macros (Automation)
GRM Analysis Wizard: Between Design
GRM Analysis Wizard Between Design - Default Between Design Tab
GRM Analysis Wizard Between Design - Custom Between Design Tab
GRM Analysis Wizard Extended Options
GRM Analysis Wizard Extended Options - Quick Tab
GRM Analysis Wizard Extended Options - Advanced Tab
GRM Quick Specs
GRM Quick Specs - Quick Tab
GRM Between Effects
GRM Quick Specs - Options Tab
Stepwise Regression Options in GRM
Best Subset Regression Options in GRM
GRM Results
GRM Results - Quick Tab
GLM, GRM, and ANOVA Results - Means Tab
GLM, GRM, and ANOVA Results - Comps Tab
GLM, GRM, and ANOVA Results - Profiler Tab
Desirability Profiling in GLM, GRM, and MANOVA
GLM, GRM, and ANOVA Results - Resids Tab
GLM, GRM, and ANOVA Results - Matrix Tab
GLM, GLZ, GRM, PLS, and ANOVA Results - Report Tab
GRM Results - Summary Tab
GRM Whole Model R Results Spreadsheets
Model Building Results in GRM
GRM More Results
GRM More Results - Summary Tab
GLM, GRM, and ANOVA More Results - Means Tab
GLM, GRM, and ANOVA More Results - Planned Comps Tab
GLM, GRM, and ANOVA More Results - Post-hoc Tab
GLM, GRM, and ANOVA More Results - Assumptions Tab
GLM, GRM, and ANOVA More Results - Profiler Tab
Desirability Profiling in GLM, GRM, and MANOVA
GLM, GRM, and ANOVA More Results - Custom Tests Tab
GLM, GRM, and ANOVA More Results - Residuals 1 Tab
GLM, GRM, and ANOVA More Results - Residuals 2 Tab
GLM, GRM, and ANOVA More Results - Matrix Tab
GLM, GRM, and ANOVA More Results - Report Tab
General Regression Models (GRM) - Examples
Example 1: Stepwise Regression
Example 2: Best-Subset Regression
GRM Syntax - Example 1: Multiple Regression
GRM Syntax - Example 2: Stepwise Multiple Regression with Categorical Predictors
GRM Syntax - Example 3: Best-Subset Regression with Categorical Predictors
GRM Syntax - Example 4: Testing Equality of Regression Coefficients
Partial Least Squares (PLS) Overview
Partial Least Squares (PLS) Overview - Basic Ideas
Partial Least Squares (PLS) Overview - Computational Approach
Partial Least Squares (PLS) Overview - Training (Analysis) and Verification (Cross-Validation) Samples
Partial Least Squares (PLS) Overview - Types of Analyses
PLS - Methods for Specifying Designs
Partial Least Squares Models Startup Panel
Partial Least Squares Models Startup Panel - Quick Tab
Quick Tab
PLS Syntax - Overview
PLS Analysis Syntax Editor
PLS Analysis Syntax Editor - Keywords Tab
PLS Analysis Syntax Editor - Specifications Tab
PLS Syntax - Keyword Intercept
PLS Syntax - Keyword EDelta
PLS Syntax - Keyword Autoscale
PLS Syntax - Keyword Maxiter
PLS Syntax - Keyword Method
PLS Syntax - Keyword Ncompo
PLS Syntax - Keyword RDelta
Alternative Method for Specifying GLM Designs in Macros (Automation)
PLS Analysis Wizard Between Design
PLS Analysis Wizard Between Design - Default Between Design Tab
PLS Analysis Wizard Between Design - Custom Between Design Tab
PLS Analysis Wizard Extended Options
PLS Analysis Wizard Extended Options - Quick Tab
PLS Analysis Wizard Extended Options - Advanced Tab
PLS Quick Specs
PLS Quick Specs - Quick Tab
PLS Between Effects
PLS Quick Specs - Options Tab
PLS Results
PLS Results - Quick Tab
PLS Results - Summary Tab
PLS Results - Observational Tab
PLS Results - Distances Tab
PLS Results - Save Tab
GLM, GLZ, GRM, PLS, and ANOVA Results - Report Tab
Example 1: Multiple Regression
Example 2: Multivariate Regression
PLS Syntax - Examples
PLS Syntax - Example 1: Multiple Regression with Categorical Predictors
PLS Syntax - Example 2: Multivariate Regression with Categorical Predictors
PLS Syntax - Example 3: Multivariate Regression with SIMPLS
Multivariate Statistical Process Control (MSPC) and Nonlinear Iterative Partial Least Squares (NIPALS) Overview
PCA/PLS Startup Panel and Quick Tab
Deployment Model
Deployment Model - Quick Tab
Deployment Model - Results Tab
PCA
PCA - Quick Tab
PCA - NIPALS Tab
PCA - Fitting Tab
PCA - Options Tab
PCA - Advanced Tab
PLS Results
PCA Results - Quick Tab
PCA Results - Plots Tab
PCA Results - Advanced Tab
PCA Results - MD Tab
PLS
PLS - Quick Tab
PLS - NIPALS Tab
PLS - Fitting Tab
PLS - Options Tab
PLS - Advanced Tab
PLS Results
PLS Results - Quality Tab
PLS Results - Plots Tab
PLS Results - Advanced Tab
PLS Results - MD Tab
PCA Example
Principal Component Analysis (PCA) and Partial Least Squares (PLS) Technical Notes
Variance Components Overview
Basic Ideas of Variance Components Analysis
Basic Ideas of Variance Components Analysis - Properties of Random Effects
Estimation of Variance Components (Technical Overview)
Estimation of Variance Components - Estimating the Variation of Random Factors
Estimation of Variance Components - Estimating Components of Variation
Estimation of Variance Components - Testing the Significance of Variance Components
Estimation of Variance Components - Singular Hessian Matrix at Point of Conversion in Maximum Likelihood Estimation
Estimation of Variance Components - Estimating the Population Intraclass Correlation
Variance Components and Mixed Model ANOVA/ANCOVA Introductory Overview - Summary
Variance Components and Mixed Model ANOVA Startup Panel
Variance Components and Mixed Model ANOVA Startup Panel - Quick Tab
Variance Components and Mixed Model ANOVA Startup Panel - Advanced Tab
Variance Components and Mixed Model ANOVA Startup Panel - Model Tab
Variance Components and Mixed Model ANOVA/ANCOVA Results
Variance Components and Mixed Model ANOVA/ANCOVA Results - Quick Tab
Variance Components and Mixed Model ANOVA/ANCOVA Results - Estimation Tab
Variance Components and Mixed Model ANOVA/ANCOVA Results - Advanced Tab
Variance Components and Mixed Model ANOVA/ANCOVA - Summary: Components of Variance Spreadsheets
Variance Components and Mixed Model ANOVA/ANCOVA Method Options
Example 1: Variance Component Estimation for a Two-Way Random Factorial Design
Example 2: Variance Component Estimation for a Four-Way Mixed Factorial Design
Example 3: Variance Component Estimation for a Hierarchically Nested Random Design
Example 4: Estimating the Population Intraclass Correlation via Variance Components
Variance Components - Computational Details
Survival Analysis Overview
Censored Observations
Analytic Techniques
Life Table Analysis
Distribution Fitting
Kaplan-Meier Product-Limit Estimator
Comparing Samples
Survival Analysis Regression Models Overview
Survival Analysis Regression Models Methods
Cox's Proportional Hazard Model
Cox's Proportional Hazard Model with Time-Dependent Covariates
Exponential Regression
Normal and Lognormal Regression
Stratified Analyses
Survival and Failure Time Analysis Startup Panel and Quick Tab
Life Table and Distribution of Survival Times
Life Table & Distribution of Survival Times - Raw Data Tab
Life Table & Distribution of Survival Times - Table of Survival Times Tab
Product-Limit (Kaplan & Meier) Analysis
Product-Limit (Kaplan & Meier) Analysis - Analysis Tab
Comparing Survival in Two Groups
Comparing Survival in Two Groups - Analysis Tab
Comparing Survival in Multiple Groups
Comparing Survival in Multiple Groups - Analysis Tab
Regression Models for Censored Data
Regression Models for Censored Data - Quick Tab
Regression Models for Censored Data - Options Tab
Model Parameter Estimation
Regression Model Estimation
Regression Model Estimation - Review Tab
Regression Model Estimation - Estimation Tab
Specify Start Values
Proportional Hazard Model with Time-Dependent Covariates
Proportional Hazard Model with Time-Dependent Covariates - Quick Tab
Proportional Hazard Model with Time-Dependent Covariates - Options Tab
Life Table and Survival Time Distribution Results
Life Table and Survival Time Distribution Results - Quick Tab
Advanced Tab
Life Table and Survival Time Distribution Results - Function Plots Tab
Product-Limit (Kaplan-Meier) Analysis Results
Product-Limit (Kaplan-Meier) Analysis Results - Quick Tab
Product-Limit (Kaplan-Meier) Analysis Results - Advanced Tab
Product-Limit (Kaplan-Meier) Analysis Results - Kaplan- Meier Plots Tab
Two-Sample Tests Results
Two-Sample Tests Results - Quick Tab
Two-Sample Tests Results - Two-Sample Tests Tab
Two-Sample Tests Results - Function Plots Tab
Comparing Survival in Multiple Groups, Results
Comparing Survival in Multiple Groups, Results - Quick Tab
Comparing Survival in Multiple Groups, Results - Advanced Tab
Comparing Survival in Multiple Groups, Results - Descriptives Tab
Regression Results
Regression Results - Quick Tab
Regression Results - Advanced Tab
Regression Results - Function Plots Tab
Independent Variable Values
Regression Results - Advanced Tab
Survival Analysis Examples - Overview and Data File
Example 1: Actuarial Life Table
Example 2: Kaplan-Meier Product-Limit Estimates
Example 3: Comparing Survival in Two or More Groups
Example 4: Regression Models
Example 5: Cox Model with Time-Dependent Covariates
Survival Analysis - Notes and Technical Information
Cox Proportional Hazards Model Overview
Computational Details Overview
Time Dependent Covariates Overview
Cox Proportional Hazards Regression
Cox Proportional Hazards Regression - Quick Tab
Cox Proportional Hazards Regression - Options Tab
Cox Proportional Hazards Regression - Advanced Tab
Define Custom Design
Define Time-Dependent Covariate/Expressions
Enter Expression for Time Dependent Covariates
Nesting for Categorical Factors
Cox Proportional Hazards Results Dialog Box
Cox Proportional Hazards Results - Quick Tab
Cox Proportional Hazards Results - Survivor Function Tab
Cox Proportional Hazards Results - Plots Tab
Example 1 - Cox Proportional Hazards Model Survival
Example 2 - Cox Proportional Hazards Model Survival - Time-Dependent
Example 3 - Cox Proportional Hazards Model - Models for Recurrent Event Data
Example 4 - Cox Proportional Hazards Model - Building and Deployment to New Data
Nonlinear Estimation Overview
Nonlinear Estimation Introductory Overview - Estimating Linear and Nonlinear Models
Nonlinear Estimation - Common Nonlinear Regression Models
Common Nonlinear Regression Models - Intrinsically Linear Regression Models
Intrinsically Nonlinear Regression Models - General Growth Model
Intrinsically Nonlinear Regression Models - Models for Binary Responses: Probit & Logit
Intrinsically Nonlinear Regression Models - General Logistic Regression Model
Intrinsically Nonlinear Regression Models - Drug Responsiveness and Half-Maximal Response
Intrinsically Nonlinear Regression Models - Discontinuous Regression Models
Major Axis Regression
Nonlinear Estimation Procedures
Nonlinear Estimation Procedures - Least Squares Estimation
Nonlinear Estimation Procedures - Loss Functions
Nonlinear Estimation Procedures - Weighted Least Squares
Nonlinear Estimation Procedures - Maximum Likelihood
Nonlinear Estimation Procedures - Maximum Likelihood and Probit/Logit Models
Nonlinear Estimation Procedures - Function Minimization Algorithms
Nonlinear Estimation - Evaluating the Fit of the Model
Nonlinear Estimation Evaluating the Fit of the Model - Proportion of Variance Explained
Nonlinear Estimation Evaluating the Fit of the Model - Goodness-of-fit Chi-square
Nonlinear Estimation Evaluating the Fit of the Model - Plot of Observed vs. Predicted Values
Nonlinear Estimation Evaluating the Fit of the Model - Normal and Half-Normal Probability Plots
Nonlinear Estimation Evaluating the Fit of the Model - Plot of the Fitted Function
Nonlinear Estimation Evaluating the Fit of the Model - Variance/Covariance Matrix for Parameters
Specifying and Testing Specific Models
Nonlinear Estimation Startup Panel and Quick Tab
User-Specified Regression, Least Squares
User-Specified Regression, Least Squares - Quick Tab
Estimated Function
General Syntax Conventions: Regression Equations
Nonlinear Estimation - Syntax (User Functions)
Example: Specifying a Regression Model that Will Evaluate Differences Across Groups
Example: Using Logical Expressions to Estimate Regression Parameters
User-Specified Regression, Custom Loss
User-Specified Regression Function - Quick Tab
Estimated Function and Loss Function
General Syntax Conventions: Regression Equations
General Syntax Conventions: Loss Function
Nonlinear Estimation - Syntax (User Functions)
Example: Specifying a Regression Model that Will Evaluate Differences Across Groups
Logistic Regression (Logit) and/or Probit Regression
Logistic Regression (Logit) and/or Probit Regression - Quick Tab
Probit/Logit Regression - Input File
Exponential Growth
Exponential Growth - Quick Tab
Breakpoint Regression
Breakpoint Regression - Quick Tab
Model Estimation
Model Estimation - Quick Tab
Model Estimation - Review Tab
Model Estimation - Advanced Tab
Specify Start Values
Specify Initial Step Sizes
Nonlinear Least Squares Model Estimation
Nonlinear Least Squares Model Estimation - Quick Tab
Nonlinear Least Squares Model Estimation - Review Tab
Nonlinear Least Squares Model Estimation - Advanced Tab
Specify Start Values
Nonlinear Estimation - Parameter Estimation
Nonlinear Estimation - Results
Nonlinear Estimation Results - Quick Tab
Nonlinear Estimation Results - Advanced Tab
Nonlinear Estimation Results - Residuals Tab
Nonlinear Estimation Results - Review Tab
Nonlinear Least Squares Results
Nonlinear Least Squares Results - Quick Tab
Nonlinear Least Squares Results - Advanced Tab
Nonlinear Least Squares Results - Residuals Tab
Nonlinear Least Squares Results - Review Tab
Example 1: Predicting Success/Failure
Example 2: Predicting Redemption of Coupons
Example 3: Predicting Recovery from Injury
Example 4: Comparing Two Learning Curves
Example 4.1: Estimating Two Different Models
Example 5: Regression in Pieces
Example 6: Estimating Drug Responsiveness (Half-Maximal Response)
Example 7: Weighted Least Squares
Example: Specifying a Regression Model that Will Evaluate Differences Across Groups
Example: Using Logical Expressions to Estimate Regression Parameters
Nonlinear Estimation Examples - Concluding Remarks
Fixed Nonlinear Regression Overview
Fixed Nonlinear Regression Startup Panel and Quick Tab
Non-linear Components Regression
Non-linear Components Regression - Quick Tab
Model Definition
Model Definition - Advanced Tab
Model Definition - Descriptives Tab
Model Definition - Stepwise Tab
Model Definition - Quick Tab
Select Dependent and Independent Variable Lists
Multiple Regression Results
Multiple Regression Results - Quick Tab
Multiple Regression Results - Advanced Tab
Multiple Regression Results - Residuals/Assumptions/Prediction Tab
Specify Values for Indep. Vars
Residual Analysis
Residual Analysis - Advanced Tab
Residual Analysis - Residuals Tab
Residual Analysis - Predicted Tab
Residual Analysis - Scatterplots Tab
Residual Analysis - Probability Plots Tab
Residual Analysis - Outliers Tab
Residual Analysis - Save Tab
Residual Analysis - Quick Tab
Residuals and Predicted Values
Review Descriptive Statistics
Review Descriptive Statistics - Quick Tab
Review Descriptive Statistics - Advanced Tab
Review Descriptive Statistics - Matrix Tab
Fixed Nonlinear Regression - Example
Multiple Regression - Notes and Technical Information
Log-Linear Analysis Overview
Two-way Frequency Tables
Multi-way Frequency Tables
The Log-Linear Model
Goodness-of-Fit
Automatic Model Fitting
Log-Linear Analysis UI
Automatic Selection of Best Model Dialog
Specify the Dimensions of the Table
Specify How to Review the Table
Specify Model to be Tested
Model Specification Dialog
Review/Save Tab
Quick Tab
Specify Model to be Tested
Automatic Selection of Best Model Dialog
Advanced Tab
Select Structural Zeros
Log-Linear Results Dialog
Log-Linear Analysis Results-Quick Tab
Summary Results Tab
More Fit Statistics Tab
Example 1: Specifying Tables Directly (Frequency Table Input)
Example 2: Log Linear Analysis of Frequency Tables (incomplete)
Notes and Technical Information Algorithms (incomplete)
Time Series Analysis Overview
Time Series Analysis Introductory Overview - General Introduction
Time Series Analysis Introductory Overview - Two Main Goals
Time Series Analysis - Identifying Patterns in Time Series Data
Identifying Patterns in Time Series Data - Systematic Pattern and Random Noise
Identifying Patterns in Time Series Data - Two General Aspects of Time Series Patterns
Identifying Patterns in Time Series Data - Trend Analysis
Identifying Patterns in Time Series Data - Analysis of Seasonality
Time Series Analysis Startup Panel
Time Series Analysis Startup Panel - Quick Tab
Time Series Analysis Startup Panel - Missing Data Tab
Time Series Analysis - Active Work Area
Single Series ARIMA
Single Series ARIMA - Advanced Tab
Single Series ARIMA Results - Autocorrelations Tab
Single Series ARIMA - Quick Tab
Time Series Analysis - Review Series Tab
Select Variables for Plot/Spreadsheet
Single Series ARIMA Results
Single Series ARIMA Results - Quick Tab
Single Series ARIMA Results - Review and Residuals Tab
Single Series ARIMA Results - Distribution of Residuals Tab
Single Series ARIMA Results - Autocorrelations Tab
Single Series ARIMA Results - Advanced Tab
Time Series Analysis - Specify the Smoothing Weights
Time Series Analysis - ARIMA
ARIMA - General Introduction
ARIMA - Two Common Processes
ARIMA - ARIMA Methodology
ARIMA - Identification
ARIMA - Parameter Estimation
ARIMA - Evaluation of the Model
Time Series Analysis - Interrupted Time Series ARIMA
Time Series Analysis - Interrupted Time Series ARIMA
Interrupted Time Series ARIMA (Intervention Analysis)
Interrupted Time Series ARIMA (Intervention Analysis) - Quick Tab
Interrupted Time Series ARIMA (Intervention Analysis) - Advanced Tab
Time Series Analysis - Autocorrelations Tab
Interrupted Time Series ARIMA (Intervention Analysis) - Review Impact Patterns Tab
 Interrupted Time Series ARIMA (Intervention Analysis) - Options Tab
Time Series Analysis - Specify Start Values
Time Series Analysis - Review Series Tab
Select Variables for Plot/Spreadsheet
Single Series ARIMA Results
Single Series ARIMA Results - Advanced Tab
Single Series ARIMA Results - Autocorrelations Tab
Single Series ARIMA Results - Distribution of Residuals Tab
Single Series ARIMA Results - Quick Tab
Single Series ARIMA Results - Review and Residuals Tab
Time Series Analysis - Impact Patterns
Time Series Analysis - Exponential Smoothing
Exponential Smoothing - General Introduction
Exponential Smoothing - Simple Exponential Smoothing
Exponential Smoothing - Choosing the Best Value for Parameter α (Alpha)
Exponential Smoothing - Indices of Lack of Fit (Error)
Exponential Smoothing - Seasonal and Non-seasonal Models With or Without Trend
Exponential Smoothing - Summary of Available Exponential Smoothing Models
Seasonal and Non-Seasonal Exponential Smoothing
Seasonal and Non-Seasonal Exponential Smoothing - Quick Tab
Seasonal and Non-Seasonal Exponential Smoothing - Advanced Tab
Time Series Analysis - Autocorrelations Tab
Seasonal and Non-Seasonal Exponential Smoothing - Grid Search Tab
Time Series Analysis - Review Series Tab
Select Variables for Plot/Spreadsheet
Seasonal and Non-Seasonal Exponential Smoothing - Automatic Search Tab
Automatic Parameter Search
Time Series Analysis - Exponential Smoothing Models
Time Series Analysis - Fast Fourier Transforms (FFT)
Fast Fourier Transforms (FFT) - General Introduction
Fast Fourier Transforms (FFT) - Computation of FFT in Time Series
Time Series Analysis - Single Spectrum (Fourier) Analysis
Spectrum Analysis - Basic Notation and Principles
Spectrum Analysis Basic Notation and Principles - Frequency and Period
Spectrum Analysis Basic Notation and Principles - The General Structural Model
Spectrum Analysis Basic Notation and Principles - A Simple Example
Spectrum Analysis Basic Notation and Principles - Periodogram
Spectrum Analysis Basic Notation and Principles - The Problem of Leakage
Spectrum Analysis Basic Notation and Principles - Padding the Time Series
Spectrum Analysis Basic Notation and Principles - Data Windows and Spectral Density Estimates
Spectrum Analysis Basic Notation and Principles - Tapering
Spectrum Analysis Basic Notation and Principles - Preparing the Data for Analysis
Spectrum Analysis Basic Notation and Principles - Results when no Periodicity in the Series Exists
Fourier (Spectral) Analysis
Fourier (Spectral) Analysis - Quick Tab
Fourier (Spectral) Analysis - Advanced Tab
Time Series Analysis - Autocorrelations Tab
Time Series Analysis - Review Series Tab
Single Series Fourier (Spectral) Analysis Results
Single Series Fourier (Spectral) Analysis Results - Quick Tab
Single Series Fourier (Spectral) Analysis Results - Review and Plot Tab
Single Series Fourier (Spectral) Analysis Results - Advanced Tab
Time Series Analysis - Specify the Smoothing Weights
Time Series Analysis - Single Series Fourier (Spectral) Analysis Summary Spreadsheet
Time Series Analysis - Cross-spectrum Analysis
Cross-spectrum Analysis - General Introduction
Cross-spectrum Analysis - Basic Notation and Principles
Cross-spectrum Analysis - Results for Each Variable
Cross-spectrum Analysis - Cross-periodogram, Cross-Density, Quadrature-density, Cross-amplitude
Cross-spectrum Analysis - Squared Coherency, Gain, and Phase Shift
Cross-spectrum Analysis - How the Example Data were Created
Fourier (Spectral) Analysis
Fourier (Spectral) Analysis - Quick Tab
Time Series Analysis - Autocorrelations Tab
Time Series Analysis - Review Series Tab
Fourier (Spectral) Analysis - Advanced Tab
Time Series Analysis - Selecting a Dependent Variable for Cross-spectrum
Single Series Fourier (Spectral) Analysis Results
Single Series Fourier (Spectral) Analysis Results - Quick Tab
Single Series Fourier (Spectral) Analysis Results - Review and Plot Tab
Single Series Fourier (Spectral) Analysis Results - Advanced Tab
Time Series Analysis - Specify the Smoothing Weights
Bivariate Fourier (Cross Spectrum) Analysis Results
Bivariate Fourier (Cross Spectrum) Analysis Results - Quick Tab
Bivariate Fourier (Cross Spectrum) Analysis Results - Advanced Tab
Bivariate Fourier (Cross Spectrum) Analysis Results - Periodogram & Density Plots Tab
Bivariate Fourier (Cross Spectrum) Analysis Results - Append Tab
Time Series Analysis - Classical Seasonal Decomposition (Census Method 1)
Classical Seasonal Decomposition (Census Method 1) - General Introduction
Classical Seasonal Decomposition (Census Method 1) - Computations
Ratios-to-Moving Averages Classical Seasonal Decomposition (Census Method I)
Ratios-to-Moving Averages Classical Seasonal Decomposition (Census Method I) - Quick Tab
Ratios-to-Moving Averages Classical Seasonal Decomposition (Census Method I) - Advanced Tab
Single Series ARIMA Results - Autocorrelations Tab
Time Series Analysis - Review Series Tab
Select Variables for Plot/Spreadsheet
Time Series Analysis - X-11 Census Method II Seasonal Adjustment
X-11 Census Method II Seasonal Adjustment - Basic Ideas and Terms of Seasonal Adjustment
X-11 Census Method II Seasonal Adjustment - The Census II Method
X-11 Census Method II Seasonal Adjustment - Result Tables Computed by the X-11 Method
X-11 Census Method II Seasonal Adjustment - Specific Description of all Result Tables Computed by the X-11 Method
X-11/Y2k Monthly Seasonal Adjustment (Census Method II)
X-11/Y2k Monthly Seasonal Adjustment (Census Method II) - Quick Tab
X-11/Y2k Monthly Seasonal Adjustment (Census Method II) - Output Tab
X-11/Y2k Monthly Seasonal Adjustment (Census Method II) - Trading Day Regression Tab
X-11/Y2k Monthly Seasonal Adjustment (Census Method II) - Extremes Tab
X-11/Y2k Monthly Seasonal Adjustment (Census Method II) - Trend Cycle Tab
Time Series Analysis - Autocorrelations Tab
X-11/Y2k Monthly Seasonal Adjustment (Census Method II) - Advanced Tab
Time Series Analysis - Currently Available Variables and Transformations
X-11/Y2k Monthly Seasonal Adjustment (Census Method II) - Seasonal Factor Curves Tab
Time Series Analysis - Moving Averages for Seasonal Factor Curves
Time Series Analysis - Review Series Tab
Time Series Analysis - Select the X-11 Tables and Charts to be Displayed/Printed
Select Variables for Plot/Spreadsheet
X-11/Y2k Quarterly Seasonal Adjustment (Census Method II)
X-11/Y2k Quarterly Seasonal Adjustment (Census Method II) - Quick Tab
Time Series Analysis - Autocorrelations Tab
X-11/Y2k Quarterly Seasonal Adjustment (Census Method II) - Advanced Tab
Time Series Analysis - Select the X-11 Tables and Charts to be Displayed/Printed
Time Series Analysis - Review Series Tab
Select Variables for Plot/Spreadsheet
Output Tables and/or Charts - A-Prior Adjustment (Monthly Seasonal Adjustment)
Output Tables and/or Charts - B-Preliminary Estimation of Trading-Day Variation (Monthly X-11) and Weights
Output Tables and/or Charts - C-Final Estimation of Trading-Day Variation (Monthly X-11) and Irregular Weights
Output Tables and/or Charts - D-Final Estimation of Seasonal Factors, Trend-Cycle, Irregular, and Seasonally Adjusted Series
Output Tables and/or Charts - E-Modified Original, Seasonally Adjusted, and Irregular Series
Output Tables and/or Charts - F-Month (Quarter) for Cyclical Dominance (MCD, QCD), Moving Average, and Summary Measures
Output Tables and/or Charts - G-Charts
Time Series Analysis - Distributed Lags Analysis
Distributed Lags Analysis - General Purpose
Distributed Lags Analysis - General Model
Distributed Lags Analysis - Almon Distributed Lag
Distributed Lags Analysis
Distributed Lags Analysis - Quick Tab
Time Series Analysis - Autocorrelations Tab
Select Variables for Plot/Spreadsheet
Time Series Analysis - Review Series Tab
Transformations of Variables
Transformation of Variables - x=f(x) Tab
Transformations of Variables - Shift Tab
Transformations of Variables - Difference, Integrate Tab
Transformations of Variables - Descriptives Tab
Transformations of Variables - Smoothing Tab
Time Series Analysis - Specify the Moving Average Weights
Transformation of Variables - x=f(x,y) Tab
Time Series Analysis - Currently Available Variables and Transformations
Transformations of Variables - Fourier Tab
Time Series Analysis - Specify the Smoothing Weights
Transformations of Variables - Review and Plot Tab
Select Variables for Plot/Spreadsheet
Transformations of Variables - Autocorrelations Tab
Select Variables (Series) for the X and Y Axes
Example 1: Transformation of Variables
Example 2: Single Series ARIMA
Example 3: Interrupted ARIMA
Example 4: Seasonal and Non-seasonal Exponential Smoothing
Example 5: Seasonal Decomposition (Census Method I)
Example 6: X-11 Seasonal Decomposition (Census Method II)
Example 7: Distributed Lags Analysis
Example 8: Spectrum (Fourier) Analysis
Time Series Analysis - General Logic of Memory Management
Time Series Analysis - Active Work Area
Time Series Analysis - Autocorrelations
Time Series Analysis - Partial Autocorrelations
Time Series Analysis - Crosscorrelations
Structural Equation Modeling Overview
The Basic Idea Behind Structural Modeling
Structural Equation Modeling and the Path Diagram
Rules for SEPATH Path Diagrams
Resolving Ambiguities in Path Diagrams
Inputting Path Diagrams with the PATH1 Language
Structural Models for Two or More Groups
Analyzing Structured Means Models, and Models with an Intercept Variable
Structural Equation Modeling Startup Panel
Structural Equation Modeling Startup Panel - Quick Tab
Structural Equation Modeling Startup Panel - Advanced Tab
Analysis Parameters
Specify Grouping Variable
Specify Number of Groups
Path Construction Tool
Path Tool - Edit Latent Variable Names
Path Tool - Enter Comment
Analysis Parameters
SEPATH Wizard - Select Wizard
Confirmatory Factor Model Wizard - Latent Variables
Confirmatory Factor Model Wizard - Correlate Factors
Confirmatory Factor Model Wizard - Correlate Residuals
SEPATH Wizard - Model Placement
Structural Modeling Wizard - Endogenous Variables
Structural Modeling Wizard - Exogenous Variables
Structural Modeling Wizard - Correlate Factors
Structural Modeling Wizard - Correlate Residuals
Structural Modeling - Define Structural Equation Paths
SEPATH Wizard - Model Placement
Monte Carlo Analysis
Monte Carlo Analysis - Quick Tab
Monte Carlo Analysis - Advanced Tab
Set Contamination Parameters
Set Monte Carlo Sample Sizes
Skewnesses for Group
Kurtoses for Group
Monte Carlo Analysis - Model Tab
Analysis Parameters
Structural Equation Modeling Results
Structural Equation Modeling Results - Quick Tab
Structural Equation Modeling Results - Advanced Tab
Iteration Results
Structural Equation Modeling Results - Assumptions Tab
Structural Equation Modeling Results - Residuals Tab
Monte Carlo Results
Monte Carlo Results - Quick Tab
Introductory Example - Confirmatory Factor Analysis
Example 1: Stability of Alienation
Example 2: A Confirmatory Factor Analysis
Example 3: Confirmatory Factor Analysis with Identifying Constraints
Example 4: Effect of Peer Influence on Ambition
Example 5: Standardized Solutions for the Effect of Peer Influence on Ambition
Example 6: Factor Analysis with an Intercept Variable
Example 7: Comparing Factor Structure in Two Groups
Example 8: Testing for Circumplex Structure
Example 9: Testing for Stability of a Correlation Matrix over Time
Example 10: A Multiple Regression Model for Home Environment and Math Achievement
Example 11: Structural Models for Home Environment and Mathematics Achievement
Example 12: Test Theory Models for Sets of Congeneric Tests
Example 13: Comparing Dependent Variances
Example 14: A Multi-Trait, Multi-Method Model
Example 15: A Longitudinal Factor Model
Example 16: A Structural Model for 10 Personality and Drug Use Variables
Example 17: A Test for Compound Symmetry
Example 18: Testing the Equality of Correlation Matrices from Different Populations
Monte Carlo Example 1: Adequacy of Sample Size and Heywood Cases in Factor Analysis
Monte Carlo Example 2: Performance of GLS and ML Estimation in the Comparison of Correlation Matrices
Monte Carlo Example 3: Bootstrapping
Monte Carlo Results Spreadsheet
Population Structures for Monte Carlo Experiments
Measures of Multivariate Kurtosis Spreadsheet
Measures of Univariate Kurtosis Spreadsheet
Measures of Univariate Skewness Spreadsheet
Model Summary Spreadsheet
Noncentrality-Based, Goodness-of-Fit Indices
Statistics in the Structural Equation Modeling Results Summary Box
Confirmatory Factor Analysis Wizard
Structural Modeling Wizard
How to Use the Path Construction Tool
Single Sample Goodness of Fit Indices
Structural Equation Modeling - Covariance Matrices vs. Correlation Matrices
Solving Iteration Problems
Solving Iteration Problems - How Iteration Procedures Work
Solving Iteration Problems - When an Iteration Procedure "Hangs Up"
Solving Iteration Problems - An Example
Technical Aspects of SEPATH
Models and Methods
Models and Methods - The LISREL Model
Models and Methods - The COSAN Model
Models and Methods - McArdle's RAM Model
Models and Methods - The Bentler- Weeks Model
Models and Methods - The SEPATH Model
Statistical Estimation
Statistical Estimation - General Properties of Discrepancy Functions
Statistical Estimation - OLS Estimation
Statistical Estimation - GLS Estimation
Statistical Estimation - Maximum Wishart Likelihood Estimation
Statistical Estimation - Iteratively Reweighted GLS Estimation
Statistical Estimation - Chi-square Test Statistics
Statistical Estimation - ADF Estimation
Model Identification
Unconstrained Minimization Techniques
Unconstrained Minimization Techniques - Steepest Descent Iterations
Unconstrained Minimization Techniques - Line Search Options
Unconstrained Minimization Techniques - Boundary Constraints
Unconstrained Minimization Techniques - Convergence Criteria
Noncentrality-Based Indices of Fit
Noncentrality-Based Indices of Fit - General Theoretical Orientation
Noncentrality-Based Indices of Fit - Noncentrality-Based Parameter Estimates and Confidence Intervals
Noncentrality-Based Indices of Fit - Steiger-Lind RMSEA Index
Noncentrality-Based Indices of Fit - Population Gamma Index
Noncentrality-Based Indices of Fit - Adjusted Population Gamma Index
Noncentrality-Based Indices of Fit - McDonald's Index of Noncentrality
Noncentrality-Based Indices of Fit - Extensions to Multiple Group Analysis
Other Indices of Fit - Jöreskog-Sörbom GFI
Other Indices of Fit - Rescaled Akaike Information Criterion
Other Indices of Fit - Schwarz's Bayesian Criterion
Other Indices of Fit - Browne-Cudeck Single Sample Cross-Validation Index
Other Indices of Fit - Independence Model Chi-square and df
Other Indices of Fit - Bentler-Bonett Normed Fit Index
Other Indices of Fit - Bentler-Bonett Non-Normed Fit Index
Other Indices of Fit - Bentler Comparative Fit Index
Other Indices of Fit - James-Mulaik-Brett Parsimonious Fit Index
Other Indices of Fit - Bollen's Rho
Other Indices of Fit - Bollen's Delta
New Method for Standardizing Endogenous Latent Variables
Analyzing Correlation Matrices
Fully Standardized Path Models
Analyzing Invariance Properties
Analyzing Invariance Properties - Types of Invariance
Analyzing Invariance Properties - Analyzing Invariance of Fitted Covariance Structures
Analyzing Invariance Properties - Reflector Matrices
Analyzing Invariance Properties - Using Reflector Matrices
Multiple Sample Models
Models with Structured Means or Intercept Variables
Indices of Skewness
Indices of Skewness and Kurtosis - Indices of Univariate Skewness
Indices of Skewness and Kurtosis - Indices of Univariate Kurtosis
Indices of Skewness and Kurtosis - Indices of Multivariate Kurtosis
Monte Carlo Data Generation Techniques
Multivariate Exploratory Techniques
Cluster Analysis Overview
Statistical Significance Testing
Area of Application
Two-Way Joining
k-Means Clustering
Joining (Tree Clustering) - Introductory Overview
Hierarchical Tree
Distance Measures
Amalgamation or Linkage Rules
Clustering Method Startup Panel and Quick Tab
Joining (Tree Clustering) Dialog
Joining (Tree Clustering) - Advanced Tab
Cluster Analysis: K-Means Clustering
K-Means Clustering Quick Tab
K-Means Clustering Advanced Tab
Cluster Analysis Two-Way Joining Dialog
Cluster Analysis Two-Way Joining Quick Tab
Cluster Analysis Two-Way Joining Advanced Tab
K-Means Clustering Results Dialog
K-Means Clustering Results Quick Tab
K-Means Clustering Results Advanced Tab
Two-Way Joining Results Dialog
Two-Way Joining Results - Quick Tab
Two-Way Joining Results - Advanced Tab
Example 1: Joining - Tree Clustering
Example 2: K-means Clustering
Factor Analysis Overview
Factor Analysis - Hierarchical Factor Analysis
Basic Idea of Factor Analysis as a Data Reduction Method
Basic Idea of Factor Analysis as a Data Reduction Method - Combining Two Variables into a Single Factor
Basic Idea of Factor Analysis as a Data Reduction Method - Principal Components Analysis
Basic Idea of Factor Analysis as a Data Reduction Method - Extracting Principal Components
Basic Idea of Factor Analysis as a Data Reduction Method - Generalizing to the Case of Multiple Variables
Basic Idea of Factor Analysis as a Data Reduction Method - How many Factors to Extract?
Basic Idea of Factor Analysis as a Data Reduction Method - Reviewing the Results of a Principal Components Analysis
Factor Analysis as a Classification Method
Factor Analysis as a Classification Method - Factor Loadings
Factor Analysis as a Classification Method - Rotating the Factor Structure
Factor Analysis as a Classification Method - Interpreting the Factor Structure
Factor Analysis as a Classification Method - Oblique Factors
Factor Analysis as a Classification Method - Hierarchical Factor Analysis
Other Issues and Statistics - Factor Scores
Other Issues and Statistics - Reproduced and Residual Correlations
Other Issues and Statistics - Matrix Ill-conditioning
Factor Analysis Startup Panel and Quick Tab
Define Method of Factor Extraction
Define Method of Factor Extraction - Quick Tab
Define Method of Factor Extraction - Advanced Tab
Define Method of Factor Extraction - Descriptives Tab
Factor Analysis Results
Factor Analysis Results - Quick Tab
Factor Analysis Results - Explained Variance Tab
Factor Analysis Results - Scores Tab
Factor Analysis Results - Loadings Tab
Select the Factors for the Plot (2D Plot of Loadings)
Select the Factors for the Plot (3D Plot of Loadings)
Factor Analysis Results - Descriptives Tab
Review Descriptive Statistics
Review Descriptive Statistics - Quick Tab
Review Descriptive Statistics - Advanced Tab
Review Descriptive Statistics - Pairwise Tab
Review Descriptive Statistics - Save Tab
Multiple Regression Analysis
Multiple Regression Analysis - Quick Tab
Multiple Regression Analysis - Advanced Tab
Multiple Regression Analysis - Descriptives Tab
Example 1: Factor Analysis
Factor Analysis - Notes and Technical Information
Methods for Factor Analysis
A Note on Box and Whisker Plots
A Note on Matrix Ill-Conditioning and the Modified Correlation Matrix
Rotational Strategies in Factor Analysis
Principal Components and Classification Analysis Overview
Principal Components & Classification Analysis - General Purpose
Principal Components & Classification Analysis - Data Reduction (empty)
Principal Components and Classification Analysis
Principal Components and Classification Analysis - Computational Details
Principal Components and Classification Analysis Startup Panel
Principal Components and Classification Analysis Startup Panel - Quick Tab
Principal Components and Classification Analysis Startup Panel - Advanced Tab
Principal Components and Classification Analysis Results
Principal Components and Classification Analysis Results - Quick Tab
Select the Factors for the Plot (2D Plot of Loadings)
Principal Components and Classification Analysis Results - Variables Tab
Principal Components and Classification Analysis Results - Cases Tab
Principal Components and Classification Analysis Results - Descriptives Tab
Principal Components & Classification Analysis Example
Canonical Analysis Overview
General Ideas
Sum Scores
Canonical Roots/Variates
Number of Roots
Extraction of Roots
Computational Methods and Results
Assumptions
Canonical Analysis UI
Model Definition
Model Definitions - Quick Tab
Descriptives Tab
Review Descriptive Statistics
Review Descriptive Statistics - Quick Tab
Advanced Tab
Matrix Tab
Results
Canonical Analysis Results - Quick Tab
Canonical Factors Tab
Canonical Factor Structures Tab
Scatterplot of Canonical Correlations
Canonical Scores Tab
Example1: Canonical Correlation
Notes and Technical Information
Reliability and Item Analysis Overview
Reliability and Item Analysis Introductory Overview - General Introduction
Reliability and Item Analysis Introductory Overview - Basic Ideas
Reliability and Item Analysis Introductory Overview - Classical Testing Model
Reliability and Item Analysis Introductory Overview - Reliability
Reliability and Item Analysis Introductory Overview - Sum Scales
Reliability and Item Analysis Introductory Overview - Cronbach's Alpha
Reliability and Item Analysis Introductory Overview - Split-Half Reliability
Reliability and Item Analysis Introductory Overview - Correction for Attenuation
Reliability and Item Analysis Introductory Overview - Designing a Reliable Scale
Reliability and Item Analysis Introductory Overview - Final Remarks
Reliability and Item Analysis Startup Panel
Reliability and Item Analysis Startup Panel - Quick Tab
Reliability and Item Analysis Startup Panel - Advanced Tab
Correlation Matrices in Reliability and Item Analysis
Reliability Results
Review Descriptive Statistics
Review Descriptive Statistics - Quick Tab
Review Descriptive Statistics - Advanced Tab
Review Descriptive Statistics - Matrix Tab
Reliability Results
Split-Half Reliability - Advanced Tab
Reliability Results - Attenuation Tab
Reliability Results - More Items? Tab
Reliability Results - How Many? Tab
Reliability Results - Quick Tab
Split-Half Reliability
Split-Half Reliability - Quick Tab
Reliability Results - Advanced Tab
Example 1: Evaluating the Reliability of Items in a Questionnaire
Example 2: Split-Half Reliability
Reliability and Item Analysis - Notes and Technical Information
Classification Trees Overview
Characteristics of Classification Trees - Hierarchical Nature of Classification Trees
Characteristics of Classification Trees - Flexibility of Classification Trees
Characteristics of Classification Trees - The Power and Pitfalls of Classification Trees
Computational Methods - Specifying the Criteria for Predictive Accuracy
Computational Methods - Selecting Splits
Computational Methods - Determining When to Stop Splitting
Computational Methods - Selecting the "Right-Sized" Tree
Prior Probabilities, the Gini Measure of Node Impurity, and Misclassification Cost
Classification Trees Introductory Overview - Comparisons with Other Classification Tree Programs
Classification Trees - Parameter Estimation
Classification Trees Startup Panel
Classification Trees Startup Panel - Quick Tab
Classification Trees Startup Panel - Advanced Tab
Classification Trees Startup Panel - Stopping Options Tab
Classification Trees Startup Panel - Sampling Options Tab
Classification Trees Startup Panel - Methods Tab
Classification Trees - Enter Values for the Prior Probabilities
Classification Trees - User Specified Misclassification Costs
Classification Trees Results
Classification Trees Results - Quick Tab
Classification Trees Results - Cross-Validation Tab
Classification Trees Results - Predicted Classes Tab
Classification Trees Results - Tree Plot Tab
Classification Trees Results - Tree Structure Tab
Classification Trees - Global CV Parameter Estimation
Classification Trees - Global Cross-Validation Tab
Example 1: Discriminant-Based Univariate Splits for Categorical Predictors
Example 2: Discriminant-Based Univariate Splits for Categorical and Ordered Predictors
Example 3: Discriminant-Based Linear Combination Splits for Ordered Predictors
Example 4: Exhaustive Search for Univariate Splits for Ordered Predictors
Classification Trees - Notes on Computational Algorithms
Correspondence Analysis Overview
Correspondence Analysis - Program Overview
Correspondence Analysis - Supplementary Points
Multiple Correspondence Analysis (MCA)
Correspondence Analysis Introductory Overview - Burt Table
Correspondence Analysis (CA): Table Specifications / Multiple Correspondence Analysis (MCA): Table Specifications
Correspondence Analysis (CA): Table Specifications Startup Panel - Correspondence Analysis (CA) Tab
Multiple Correspondence Analysis (MCA): Table Specifications Startup Panel - Multiple Correspondence Analysis (MCA) Tab
Specify the Dimensions of the Table
Input Formats in Correspondence Analysis - Raw Data
Input Formats in Correspondence Analysis - Frequencies with Grouping Variables
Input Formats in Correspondence Analysis - Frequencies without Grouping Variables
Select the Dimensions to Plot - 1D Plot of Points
Select the Dimensions to Plot - 2D Plot of Points
Select the Dimensions to Plot - 3D Plot of Points
Correspondence Analysis Results
Correspondence Analysis Results - Quick Tab
Row and Column Coordinates in Correspondence Analysis
Correspondence Analysis Results - Advanced Tab
Correspondence Analysis Results - Options Tab
Correspondence Analysis Results - Review Tab
Correspondence Analysis Results - Supplementary Points Tab
Supplementary Row/Column Points
Multiple Correspondence Analysis Results
Multiple Correspondence Analysis Results - Advanced Tab
Multiple Correspondence Analysis Results - Options Tab
Multiple Correspondence Analysis Results - Review Tab
Multiple Correspondence Analysis Results - Quick Tab
Column Coordinates in Multiple Correspondence Analysis
Example 1: Correspondence Analysis and Supplementary Points
Example 2.1: Analyzing an Indicator Matrix (Consumer Preferences)
Example 2.2: Multiple Correspondence Analysis
Example 3: Protein Consumption in Europe
Correspondence Analysis - Computational Details
Multidimensional Scaling Overview
Multidimensional Scaling Introductory Overview - Logic of MDS
Multidimensional Scaling Introductory Overview - Computational Approach
Multidimensional Scaling Introductory Overview - How Many Dimensions to Specify?
Multidimensional Scaling Introductory Overview - Interpreting the Dimensions
Multidimensional Scaling Introductory Overview - Applications
Multidimensional Scaling Introductory Overview - MDS and Factor Analysis
Multidimensional Scaling Startup Panel
Multidimensional Scaling Startup Panel - Quick Tab
Multidimensional Scaling Startup Panel - Options Tab
MDS Results
MDS Results - Advanced Tab
MDS Results - Review and Save Tab
MDS Results - Quick Tab
Select Two Dimensions for Scatterplot
Select Three Dimensions for Scatterplot
Multidimensional Scaling - Example
MDS - Notes and Technical Information
Parameter Estimation
Discriminant Function Analysis Overview
Discriminant Function Analysis Introductory Overview - Computational Approach
Discriminant Function Analysis Introductory Overview - Stepwise Discriminant Analysis
Discriminant Function Analysis Introductory Overview - Interpreting a Two-Group Discriminant Function
Discriminant Function Analysis Introductory Overview - Discriminant Functions for Multiple Groups
Discriminant Function Analysis Introductory Overview - Assumptions
Discriminant Function Analysis Introductory Overview - Classification
Discriminant Function Analysis Startup Panel and Quick Tab
Model Definition
Model Definition - Quick Tab
Model Definition - Advanced Tab
Model Definition - Descriptives Tab
Review Descriptive Statistics
Review Descriptive Statistics - Quick Tab
Review Descriptive Statistics - Within Tab
Review Descriptive Statistics - All Cases Tab
Discriminant Function Analysis Results
Discriminant Function Analysis Results - Quick Tab
Discriminant Function Analysis Results - Advanced Tab
Discriminant Function Analysis Results - Classification Tab
Specify A Priori Classification Probabilities
Canonical Analysis
Canonical Analysis - Quick Tab
Canonical Analysis - Advanced Tab
Canonical Analysis - Canonical Scores Tab
Select Group for Histogram
Discriminant Function Analysis - Example
Discriminant Function Analysis Notes and Technical Information - General
Discriminant Function Analysis Notes and Technical Information - Formulas
Discriminant Function Analysis Notes and Technical Information - Testing Complex Hypotheses Using SEPATH
General Discriminant Analysis Models Overview
GDA Introductory Overview - Coding the Categorical Dependent Variable
GDA Introductory Overview - Advantages of GDA
GDA Introductory Overview - Comparison with Other Stepwise Discriminant Analysis Programs
GDA Introductory Overview - Unique Features
GDA - Methods for Specifying Designs
General Discriminant Analysis (GDA) Models Startup Panel
General Discriminant Analysis (GDA) Models Startup Panel and Quick Tab
GDA Syntax - Overview
General Discriminant Analysis (GDA) Models Syntax Editor
General Discriminant Analysis (GDA) Models Syntax Editor - Keywords Tab
General Discriminant Analysis (GDA) Models Syntax Editor - Specifications Tab
GDA Syntax - Keyword Dependent
GDA Syntax - Keyword Priors
GRM and GDA Syntax - Stepwise Regression
Best Subset Regression
GRM and GDA Syntax - Options for MBUILD
Alternative Method for Specifying GLM Designs in Macros (Automation)
GDA Models Wizard Effects in Design
GDA Models Wizard Effects in Design - Default Design Tab
GDA Models Wizard Effects in Design - Custom Design Tab
GDA Models Wizard Extended Options
GDA Models Wizard Extended Options - Quick Tab
GDA Models Wizard Extended Options - Advanced Tab
GDA - Select Apriori Classification Probabilities
Best Subset Discriminant Analysis Options in GDA
GDA Quick Specs
GDA Quick Specs - Quick Tab
GDA Effects in Design
GDA Quick Specs - Advanced Tab
GDA - Select Apriori Classification Probabilities
Stepwise Discriminant Analysis Options in GDA
Best Subset Discriminant Analysis Options in GDA
GDA Models Results
GDA Models Results - Quick Tab
GDA Models Results - Functions Tab
GDA Models Results - Cases Tab
GDA Models Results - Effects Tab
GDA Models Results - Profiler Tab
GDA - Response/Desirability Profiler
Options for Response Profiler
GDA - Classes for Residuals
GDA Models Results - Regression Residuals 1 Tab
GDA - Classes for Residuals
GDA Models Results - Regression Residuals 2 Tab
GDA - Classes for Residuals
GDA Models Results - Matrices Tab
GDA Models Results - Report Tab
General Discriminant Analysis (GDA) Models - Examples
GDA - Example 1: Stepwise Discriminant Analysis
GDA - Example 2: Best-Subset Discriminant Analysis
GDA Syntax - Examples
GDA Syntax - Example 1: Simple Standard Discriminant Analysis
GDA Syntax - Example 2: Stepwise Discriminant Analysis with Categorical Predictors
Industrial Statistics and Six Sigma
Quality Control Charts
Quality Control Charts Overview
The Architecture of the Quality Control Charts Module
Quality Control Introductory Overview- General Purpose
Quality Control Introductory Overview - General Approach
Quality Control Introductory Overview - Establishing Control Limits
Quality Control Introductory Overview - Common Types of Charts
Quality Control Introductory Overview - Short Run Charts
Quality Control Events
List of Chart Events
Quality Control Introductory Overview - Unequal Sample Sizes
Quality Control Introductory Overview - Control Charts for Variables vs. Charts for Attributes
Quality Control Introductory Overview - Control Chart for Individual Observations
Quality Control Introductory Overview - Out-of-Control Process: Runs Tests
Quality Control Introductory Overview - Operating Characteristic (OC) Curves
Quality Control Introductory Overview - Process Capability Indices
"Six Sigma" Methodology and Statistica
Measurements Related to Product Quality: Custom Alarm Handling and Custom SVB Scripts
Types of Quality Control Charts Analyses
Attributes Charts
Defining Variables for Attribute Charts
Defining Variables for Attribute Charts - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Variables for (C) Attribute Charts
Defining Variables for C (Attribute) Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Sets, Specifications for Charts from Variables
Defining Sets, Specifications for Charts from Variables - X(X-Bar) tab
Defining Sets, Specifications for Charts from Variables - R (MR, S) Tab
Quality Control Charts - Reviewing Attribute Control Charts Results
Attribute Charts Results - Brushing Tab
Attribute Charts Results - Charts Tab
Attribute Charts Options - Stats Tab
Attribute Charts Results - Parts Tab
Attribute Charts Results - Specs Tab
Attribute Charts - Options
Attribute Charts Options - Layout Tab
Attribute Charts Options - Scaling Tab
Pareto Charts
Defining Variables for Pareto Charts
Defining Variables for Pareto Charts - Quick Tab
Defining Variables for Pareto Charts - Labels, Causes, Actions Tab
Reviewing Pareto Charts Results
Pareto Charts Results - Quick Tab
Pareto Charts Results - Options
Pareto Chart Options - Layout Tab
Pareto Chart Options - Shop Floor Tab
Variables Charts
Defining Variables for X-bar and Variability Chart
Defining Variables for X-bar and Variability Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Variables for X and Moving Range Chart and CUSUM Chart
Defining Variables for X and Moving Range Chart and CUSUM Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Variables for EWMA X-bar and R/S Charts
Defining Variables for MA X-bar and R/S Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Variables for EWMA X-bar and R/S Charts
Defining Variables for MA X-bar and R/S Chart - Quick Tab
Defining Variables - Sets Tab
Defining Variables - Labels, Causes, Actions Tab
Defining Sets, Specifications for Charts from Variables
Defining Sets, Specifications for Charts from Variables - X(X-Bar) tab
Defining Sets, Specifications for Charts from Variables - R (MR, S) Tab
Reviewing Variable Control Charts, Moving Average Charts, and X & Moving Range Charts Results
Results - R/S-specs Tab
Results - Brushing Tab
Results - Charts Tab
Results - Non-Normal Tab
Results - Parts Tab
Results - Report Tab
Results - Sets Tab
Results - X-Specs Tab
Results - Options
Options - Alarm Tab
Options - Labeling Tab
Options - Layout Tab
Options - Patterns Tab
Options - Scaling Tab
Options - Shop Floor Tab
Options - Stats Tab
Quality Control Charts Dialogs
Quality Control Charts Startup Panel
Quality Control Charts Startup Panel - Quick Tab
Quality Control Charts Startup Panel - Variables Tab
Quality Control Charts Startup Panel - Attributes Tab
Quality Control Charts Startup Panel - Real-Time Tab
Add Variables
Assigning a Cause, Action, or Comment
Brushing Commands
Brushing Commands
Causes, Actions, Comments, Data Brushing Setup
Chart Center Line
Control Limit Dialogs
Define Actions Resulting From
Edit Text Label
Locking Analysis for Shop Floor
Runs Tests for Control Chart
Sample
Select Specifications to be Saved
Select the Contents and Layout for the Six Graph Summary
Sigma for
Sigma Multiplier for Detecting Shift
Specifications for Capability Analysis
Specify Program (Command Line) and Optional Parameters
Specify Target Values
Specify Text Labels For
Specify Type of Moving Average
Warning Lines
Label (Name) for the New Set of Samples
Compute the Set from Ranges or Codes
Specify the Range of Samples for Computing the Set Statistics
Specify the Coding Variable and Code for the Computations
Apply the Set Statistics and Specifications to Ranges or . . .
Specify the Application Range for the Set
Quality Control Events
Example 1: A Simple Custom Chart Event Handler
Example 2: Custom Response to Specific Alarms
Example 3: Changing the Chart Specifications from Inside Event Handlers
Example 4: Computing Other Statistics or Graphs for Each Chart Update
List of Chart Events
Measurements Related to Product Quality: Custom Alarm Handling and Custom SVB Scripts
Quality Control Charts Examples
QC Charts Example 1: Specifying Control Charts for Variables, Process Capability
QC Charts Example 2: Brushing, Assigning Causes and Actions
QC Charts Example 3: Computing Chart Parameters for Different Sets
QC Charts Example 4: Setting Up Shop Floor Mode and Alarm Notification
QC Charts Example 5: Specifying Control Charts for Attributes, Custom Control Limits
QC Charts Example 6: Specifying Pareto Charts, Changing the Project Header, Printing Results
QC Charts Example 7: Specifying Moving Average/Moving Range charts
QC Charts Example 8: Specifying Charts for Individual Observations and Moving Ranges
QC Charts Example 9: Using IQC on the Shop Floor
QC Charts Example 10: Specifying Control Charts for Short Production Runs
QC Charts Example 11: Specifying CuSum Charts
Quality Control Charts Overview
The Architecture of the Quality Control Charts Module
The Nature of Sets
Auto-Updating of Statistics Displayed in Charts and Spreadsheets
Quality Control Charts - Computational Details
Evaluation of D2(N), D3(N), C4(N), and Related Statistics
Short Run Transformations
Process Analysis
Process Analysis Overview
Statistica Gage Linearity Overview
Process Analysis - Process (Machine) Capability Analysis - Introductory Overview
Process Analysis - Process (Machine) Capability Analysis - Computational Approach
Process Analysis - Process (Machine) Capability Analysis - Process Capability Indices
Process Analysis - Process (Machine) Capability Analysis - Process Performance vs. Process Capability
Process Analysis - Process (Machine) Capability Analysis - Using Experiments to Improve Process Capability
Process Analysis - Process (Machine) Capability Analysis - Testing the Normality Assumption
Process Analysis - Process (Machine) Capability Analysis - Tolerance Limits
Normal and General Non-Normal (Pearson and Johnson Fitting by Moments)
Process Analysis - Non-Normal Distributions
Non-Normal Distributions - Introductory Overview
Non-Normal Distributions - Beta Distribution
Non-Normal Distributions - Exponential Distribution
Non-Normal Distributions - Extreme Value Distribution
Non-Normal Distributions - Gamma Distribution
Non-Normal Distributions - Lognormal Distribution
Non-Normal Distributions - Rayleigh Distribution
Non-Normal Distributions - Weibull Distribution
Non-Normal Distributions - Fitting Distributions by Moments
Non-Normal Distributions - Assessing the Fit: Quantile and Probability Plots
Non-Normal Distributions - Non-Normal Process Capability Indices (Percentile Method)
Overview of Time-Dependent Distribution Models
The Models
Identifying Models
Tests of Normality
Computational Approaches for Process Capability
Capability Ratios for True Position - Introductory Overview
Capability Ratios for True Position - Computational Details
Process Analysis Gage Repeatability and Reproducibility - Introductory Overview
Process Analysis Gage Repeatability and Reproducibility - Computational Approach
Process Analysis Gage Repeatability and Reproducibility - Plots of Repeatability and Reproducibility
Process Analysis Gage Repeatability and Reproducibility - Components of Variance
Process Analysis Gage Repeatability and Reproducibility - Summary
Attribute Gage Study (Analytic Method) - Introductory Overview
Attribute Gage Study (Analytic Method) - Computational Details
Attribute Agreement Overview
MSA Attribute Data Overview
Capability Analysis - Binomial and Poisson - Computational Details
Weibull and Reliability/Failure Time Analysis - Introductory Overview
Weibull and Reliability/Failure Time Analysis - General Purpose
Weibull and Reliability/Failure Time Analysis - The Weibull Distribution
Weibull and Reliability/Failure Time Analysis - Censored Observations
Weibull and Reliability/Failure Time Analysis - Two- and three-parameter Weibull distribution
Weibull and Reliability/Failure Time Analysis - Parameter Estimation
Weibull and Reliability/Failure Time Analysis - Goodness-of-Fit Indices
Weibull and Reliability/Failure Time Analysis - Interpreting Results
Weibull and Reliability/Failure Time Analysis - Grouped Data
Process Analysis Sampling Plans - General Purpose
Process Analysis Sampling Plans - Computational Approach
Process Analysis Sampling Plans - Means for H0 and H1
Process Analysis Sampling Plans - Alpha and Beta Error Probabilities
Process Analysis Sampling Plans - Fixed Sampling Plans
Process Analysis Sampling Plans - Sequential Sampling Plans
Process Analysis Sampling Plans - Summary
Process Analysis - Cause-and-Effect Diagrams
How to Specify the Data for the Cause-and-Effect Diagram
Process Analysis Procedures Startup Panel and Quick Tab
Process Capability Analysis Setup--Raw Data
Process Capability Analysis Setup--Raw Data - Raw Data Tab
Enter/Edit Specification Limits
Process Capability Analysis Setup--Raw Data - Grouping Tab
Process Capability Analysis Setup--Raw Data - Distribution Tab
Process Capability Analysis Setup--Aggregated Data
Process Capability Analysis Setup--Aggregated Data - Advanced Tab
Process Capability Analysis Setup--Aggregated Data - Quick Tab
Enter/Edit Specification Limits
ISO 21747 / DIN 55319 Process Capability Setup
ISO 21747 / DIN 55319 Process Capability Setup Dialog and Quick Tab
ISO 21747 / DIN 55319 - Process Capability Setup - Grouping Tab
ISO 21747 / DIN 55319 - Process Capability Setup - Distributions Tab
ISO 21747 / DIN 55319 - Process Capability Setup - Outliers & Tests Tab
ISO 21747 / DIN 55319 - Process Capability Setup - Normality Tab
ISO 21747 / DIN 55319 - Process Capability Setup - Method Option Tab / Results - Method Option Tab
ISO 21747 - Process Capability Setup - Options M1 (l,d) Tab / DIN 55319 - Process Capability Setup - Options M3, M4 Tab
ISO 21747 Process Capability Setup - Options M2, M3 Tab / DIN 55319 Process Capability Setup - Options M5, M6 Tab
Capability Ratios for True Position
Capability Ratios for True Position - Specification Tab
Repeatability & Reproducibility Analysis
Repeatability & Reproducibility Analysis - Generate Design Tab
Repeatability & Reproducibility Analysis - Analyze Data File Tab
Repeatability & Reproducibility Analysis - Analyze R & R Data Sheet Tab
Gage Capability
Gage Capability - Generate Design Tab
Gage Capability - Analyze Data File Tab
Gage Capability - Options Tab
Gage Linearity
Attribute Gage Study (Analytic Method) - Attribute Gage Study Tab
Attribute Agreement Analysis - Advanced Tab
MSA Attribute Data
MSA Attribute Data - MSA Attribute Data Tab
MSA Attribute Data - Acceptance Options Tab
Capability Analysis (Binomial)
Capability Analysis (Binomial) - Quick Tab
Capability Analysis (Binomial) - Runs Tests Tab
Capability Analysis (Poisson Distribution)
Capability Analysis (Poisson) - Quick Tab
Capability Analysis (Poisson Distribution) - Runs Tests Tab
Weibull & Reliability/Failure Time Analysis
Weibull & Reliability/Failure Time Analysis - Raw Data Tab
Weibull & Reliability/Failure Time Analysis - Grouped Data Tab
Weibull Probability Paper
Weibull Probability Paper - Variables (Optional) Tab
Sampling Plans
Sampling Plans - Quick Tab
Sampling Plans - Advanced Tab
Cause-and-Effect (Ishikawa) Diagrams
Cause-and-Effect (Ishikawa) Diagrams - Quick Tab
Cause-and-Effect (Ishikawa) Diagrams - Arrows Tab
Cause-and-Effect (Ishikawa) Diagrams - Font Sizes Tab
Reviewing Process Analysis Results
Process Capability Analysis
Process Capability Analysis--Normal and General Non-Normal Distribution (Raw Data)
Process Capability Analysis--Normal and General Non-Normal Distribution (Raw Data) - Quick Tab
Process Capability Analysis--Normal and General Non-Normal Distribution (Raw Data) - Specs Tab
Process Capability Analysis--Normal and General Non-Normal Distribution (Raw Data) - Advanced, Normal Tab
Process Capability Analysis--Normal and General Non-Normal Distribution (Raw Data) - Advanced, Non-Normal Tab
Process Capability Analysis Normal and General Non-Normal Distribution (Raw Data) Tolerance Limits Tab
Process Capability Analysis--Normal and General Non-Normal Distribution (Raw Data) - Options Tab
Process Capability Analysis Normal and General Non-Normal Distribution (Aggregated Data)
Process Capability Analysis Normal and General Non-Normal Distribution (Aggregated Data) - Quick Tab
Process Capability Analysis Distribution (Raw Data)
Process Capability Analysis Distribution (Raw Data) - Quick Tab
Process Capability Analysis Distribution (Raw Data) - Specifications Tab
Process Capability Analysis Distribution (Raw Data) - Advanced Tab
Process Capability Analysis Distribution (Raw Data) - Options Tab
ISO 21747 / DIN 55319 - Process Capability Results
ISO 21747 / DIN 55319 Process Capability Results - Quick Tab
ISO 21747 / DIN 55319 Process Capability Results - Specs Tab
ISO 21747 / DIN 55319 - Process Capability Setup - Method Option Tab / Results - Method Option Tab
ISO 21747 - Process Capability Setup - Options M1 (l,d) Tab / DIN 55319 - Process Capability Setup - Options M3, M4 Tab
ISO 21747 Process Capability Setup - Options M2, M3 Tab / DIN 55319 Process Capability Setup - Options M5, M6 Tab
Capability Ratios for True Position Results
Capability Ratios for True Position Results Dialog - Results Tab
Gage R and R
Repeatability & Reproducibility Design
Repeatability & Reproducibility Design - Data Files Tab
Repeatability & Reproducibility Design - R & R Data Sheets Tab
Gage Repeatability & Reproducibility Results
Gage Repeatability & Reproducibility Results - Quick Tab
Gage Repeatability & Reproducibility Results - Advanced Tab
Gage Repeatability & Reproducibility Results - Descriptives/Plots Tab
Gage Repeatability & Reproducibility Results - Gage Performance Tab
Gage Repeatability & Reproducibility Results - Options Tab
Gage Capability
Gage Capability Design Results - Data Files Tab
Gage Capability Results
Gage Capability Results - Quick Tab
Gage Capability Results - Descriptives/Plots Tab
Gage Capability Results - Specs Tab
Gage Capability Results - Options Tab
Gage Linearity Results
Attribute Gage Study Results
Attribute Agreement Results
Attribute Agreement Results - Quick Tab
Attribute Agreement Results - Advanced Tab
Attribute Data MSA Results Dialog Box and Quick Tab
Capability Analysis (Binomial) Results
Capability Analysis (Binomial) Results - Quick Tab
Capability Analysis (Binomial) Results - Advanced Tab
Capability Analysis (Poisson Distribution) Results
Capability Analysis (Poisson Distribution) Results - Quick Tab
Capability Analysis (Poisson Distribution) Results - Advanced Tab
Weibull Analysis
Weibull Analysis: Results (Raw Data)
Weibull Analysis: Results (Raw Data) - Quick Tab
Weibull Analysis: Results (Raw Data) - Advanced Tab
Weibull Analysis: Results (Raw Data) - Reliability & Distribution Function Tab
Weibull & Reliability Analysis - Failure Order for Single or Uncensored Data
Weibull & Reliability Analysis - Modified Failure Order for Multiple-Censored Data
Weibull Analysis: Results (Grouped Data)
Weibull Analysis: Results (Grouped Data) - Quick Tab
Weibull Analysis: Results (Grouped Data) - Estimate Tab
Weibull Analysis: Results (Grouped Data) - Reliability Tab
Weibull Probability Paper
Weibull Probability Paper - Quick Tab
Weibull Probability Paper - Advanced Tab
Sampling Plans Results
Sampling Plans Results - Sequential Sampling Plan Tab
Sampling Plans Results - Fixed Sampling Plan Tab
Specify Sample Size
Introduction to Process Analysis Examples
Example 1: Deriving and Examining a Sampling Plan
Example 2: Process Capability Analysis
Example 3: Gage Repeatability and Reproducibility
Example 4: Weibull and Reliability/Failure Time Analysis
Example 5: Gage Linearity and Bias Study
Example 6: Attribute Agreement Analysis
Example 7: Capability Ratios for True Position
Example 8: MSA Attribute Data
Example 9: Attribute Gage Study (Analytic Method)
Example 10: Capability Analysis (Binomial)
Example 11: Unstacking Data in Attribute Agreement Analysis
Repeatability and Reproducibility - Components of Variance, Percent of Tolerance, and Total Variation
Statistica Gage Linearity Technical Notes
Process Analysis - General Algorithms and Formulas
Non-Normal Distribution Fitting - Notes and Technical Information
Z Benchmark Potential
Weibull and Reliability/Failure Time Analysis - Notes and Technical Information
Weibull and Reliability Analysis - Weibull CDF, Reliability, and Hazard Functions
Experimental Design
Experimental Design Overview
Experimental Design - General Ideas
Experimental Design - Experiments in Science and Industry
Bayesian Reliability Optimization for Continuous/Binary Response Overview
Experimental Design - Computational Problems
Experimental Design - Components of Variance, Denominator Synthesis
Experimental Design - Summary
Design & Analysis of Experiments Startup Panel
Design & Analysis of Experiments Startup Panel - Quick Tab
Design & Analysis of Experiments Startup Panel - Advanced Tab
Design & Analysis of Experiments with Two-Level Factors
Design & Analysis of  Experiments with Two-Level Factors - Design Experiment Tab
Design & Analysis of Experiments with Two-Level Factors - Analyze Design Tab
Design of an Experiment with Two-Level Factors
Design of a 2 and/or 3-level Screening Experiment - Quick Tab
Design of a 2 and/or 3-level Screening Experiment - Display Design Tab
Design of an Experiment with Two-Level Factors - Add To Design Tab
Design of an Experiment with Two-Level Factors - Generator and Aliases Tab
Analysis of an Experiment with Two-Level Factors
Analysis of an Experiment with Two-Level Factors - Design Tab
Analysis of an Experiment with Two-Level Factors - ANOVA/Effects Tab
Analysis of an Experiment with Two-Level Factors - Review/Save Residuals Tab
Analysis of an Experiment with Two-Level Factors - Residual Plots Tab
Analysis of an Experiment with Two-Level Factors - Box-Cox Tab
Analysis of an Experiment with Two-Level Factors - Quick Tab
Factors for Cube Plot
Factors for Square Plot
Compute Marginal Means for
Specify the Arrangement of the Factors in the Plot
Analysis of an Experiment with Two-Level Factors - Model Tab
Customized (Pooled) Error Term
Analysis of an Experiment with Two-Level Factors - Means Tab
Compute Marginal Means for
Factors for Cube Plot
Factors for Square Plot
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Analysis of an Experiment with Two-Level Factors - Prediction and Profiling Tab
Select Factor Values (When Creating Surface or Contour Plots).
Select Factor Values (When Predicting Dependent Variable Values)
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Marginal Means for Experiments with Two-Level Factors
Main Effects and Interactions for Experiments with Two-Level Factors
2-level Screening Designs
Design & Analysis of Screening Experiments
Design & Analysis of Mixture Experiments - Design Experiment Tab
Design & Analysis of Screening Experiments - Analyze Design Tab
Design of a Screening (Plackett-Burman) Experiment
Design of a Screening (Plackett-Burman) Experiment - Quick Tab
Design of a Screening (Plackett-Burman) Experiment - Display Design Tab
Design of a Screening (Plackett-Burman) Experiment - Add to Design Tab
Design of a Screening (Plackett-Burman) Experiment - Generators and Aliases Tab
Analysis of a Screening Experiment with Two-Level Factors
Analysis of a Screening Experiment with Two-Level Factors - Design Tab
Analysis of a Screening Experiment with Two-Level Factors - ANOVA/Effects Tab
Analysis of a Screening Experiment with Two-Level Factors - Review/Save Residuals Tab
Analysis of a Screening Experiment with Two-Level Factors - Residual Plots Tab
Analysis of a Screening Experiment with Two-Level Factors - Box-Cox Tab
Analysis of a Screening Experiment with Two-Level Factors - Quick Tab
Factors for Cube Plot
Factors for Square Plot
Compute Marginal Means for
Specify the Arrangement of the Factors in the Plot
Analysis of a Screening Experiment with Two-Level Factors - Model Tab
Customized (Pooled) Error Term
Analysis of a Screening Experiment with Two-Level Factors - Means Tab
Factors for Cube Plot
Factors for Square Plot
Compute Marginal Means for
Specify the Arrangement of the Factors in the Plot
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Analysis of a Screening Experiment with Two-Level Factors - Prediction and Profiling Tab
Select Factor Values (When Predicting Dependent Variable Values)
Select Factor Values (When Creating Surface or Contour Plots).
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Marginal Means for Screening (Plackett-Burman) Experiments
Main Effects and Interactions for Screening (Plackett-Burman) Experiments
2(k-p) Maximally Unconfounded and Minimum Aberration Designs
Searching for and Analyzing Designs with Two-Level Factors
Searching for and Analyzing Designs with Two-Level Factors - Search For Design Tab
Searching for and Analyzing Designs with Two-Level Factors - Search Strategy Tab
Searching for and Analyzing Designs with Two-Level Factors - Analyze Design Tab
Searching for and Analyzing Designs with Two-Level Factors - Search Criterion Tab
Experimental Design - Unconfounded Effects
Searching for Designs
Design of an Experiment with Two-Level Factors
Design of a 2 and/or 3-level Screening Experiment - Quick Tab
Design of a 2 and/or 3-level Screening Experiment - Display Design Tab
Design of an Experiment with Two-Level Factors - Add To Design Tab
Design of an Experiment with Two-Level Factors - Generator and Aliases Tab
Analysis of an Experiment with Two-Level Factors
Analysis of an Experiment with Two-Level Factors - Design Tab
Analysis of an Experiment with Two-Level Factors - ANOVA/Effects Tab
Analysis of an Experiment with Two-Level Factors - Review/Save Residuals Tab
Analysis of an Experiment with Two-Level Factors - Residual Plots Tab
Analysis of an Experiment with Two-Level Factors - Box-Cox Tab
Analysis of an Experiment with Two-Level Factors - Quick Tab
Factors for Cube Plot
Factors for Square Plot
Compute Marginal Means for
Specify the Arrangement of the Factors in the Plot
Analysis of an Experiment with Two-Level Factors - Model Tab
Customized (Pooled) Error Term
Analysis of an Experiment with Two-Level Factors - Means Tab
Compute Marginal Means for
Factors for Cube Plot
Factors for Square Plot
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Analysis of an Experiment with Two-Level Factors - Prediction and Profiling Tab
Select Factor Values (When Creating Surface or Contour Plots).
Select Factor Values (When Predicting Dependent Variable Values)
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
3**(k-p) and Box-Behnken Designs
Design & Analysis of Experiments with Three-Level Factors
Design & Analysis of Experiments with Three-Level Factors - Design Experiment Tab
Design & Analysis of Experiments with Three-Level Factors - Analyze Design Tab
Design of an Experiment with Three-Level Factors
Design of a 2 and/or 3-level Screening Experiment - Quick Tab
Design of an Experiment with Three-Level Factors - Display Design Tab
Design of an Experiment with Three-Level Factors - Add to Design Tab
Design of an Experiment with Three-Level Factors - Generator & Aliases Tab
Analysis of an Experiment with Three-Level Factors
Analysis of an Experiment with Three-Level Factors - Design Tab
Analysis of an Experiment with Three-Level Factors - ANOVA/Effects Tab
Analysis of an Experiment with Three-Level Factors - Review/Save Residuals Tab
Analysis of an Experiment with Three-Level Factors - Residual Plots Tab
Analysis of an Experiment with Three-Level Factors - Box-Cox Tab
Analysis of an Experiment with Three-Level Factors - Quick Tab
Compute Marginal Means for
Specify the Arrangement of the Factors in the Plot
Analysis of an Experiment with Three-Level Factors - Model Tab
Customized (Pooled) Error Term
Analysis of an Experiment with Three-Level Factors - Means Tab
Compute Marginal Means for
Specify the Arrangement of the Factors in the Plot
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Analysis of an Experiment with Three-Level Factors - Prediction & Profiling Tab
Select Factors for 3D Plot
Select Factor Values (When Predicting Dependent Variable Values)
Select Factor Values (When Creating Surface or Contour Plots).
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Marginal Means in Experiments with Three-Level Factors
Experimental Design - Critical Values for Experiments with Three-Level Factors
Main Effects and Interactions for Experiments with Three-Level Factors
ANOVA Tables for Experiments with Three-Level Factors
Bayesian Reliability Optimization for Continuous/Binary Response Overview
Bayesian Reliability Optimization for Continuous Response Node
Central Composite, Non-Factorial, Surface Designs
Design & Analysis of Central Composite (Response Surface) Experiments
Design & Analysis of Central Composite (Response Surface) Experiments - Design Experiment Tab
Design & Analysis of Central Composite (Response Surface) Experiments - Analyze Design Tab
Factor Levels for Recoding Factor Values
Design of a Central Composite (Response Surface) Experiment
Design of a Central Composite (Response Surface) Experiment - Quick Tab
Design of a Central Composite (Response Surface) Experiment - Display Design Tab
Design of a Central Composite (Response Surface) Experiment - Add to Design Tab
 Design of a Central Composite (Response Surface) Experiment - Design Characteristics Tab
Design of a Central Composite (Response Surface) Experiment - Generators & Aliases Tab
Analysis of a Central Composite (Response Surface) Experiment
Analysis of a Central Composite (Response Surface) Experiment - Quick Tab
Analysis of a Central Composite (Response Surface) Experiment - ANOVA/Effects Tab
Analysis of a Central Composite (Response Surface) Experiment - Review/Save Residuals Tab
Analysis of a Central Composite (Response Surface) Experiment - Residual Plots Tab
Analysis of a Central Composite (Response Surface) Experiment - Box-Cox Tab
Analysis of a Central Composite (Response Surface) Experiment - Model Tab
Customized (Pooled) Error Term
Analysis of a Central Composite (Response Surface) Experiment - Design Tab
Factor Levels for Recoding Factor Values
Analysis of a Central Composite (Response Surface) Experiment - Means Tab
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Analysis of a Central Composite (Response Surface) Experiment - Prediction & Profiling Tab
Select Factor Values (When Predicting Dependent Variable Values)
Select Factor Values (When Creating Surface or Contour Plots).
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Experimental Design - Critical Values for Central Composite Designs
Main Effects and Interactions for Central Composite Experiments
Constructing D- and A-Optimal Designs
D- and A-Optimal Designs
D- and A-Optimal Designs - Model Tab
D- and A-Optimal Designs - Optimization Methods Tab
D- and A-Optimal Designs - Options Tab
D- and A-Optimal Designs - Candidate Points Tab
List of Candidate Points for Optimal Design
Optimal Design Result: Mixture (No Intercept)
Optimal Design Result: Mixture (No Intercept) - Display design Tab
Optimal Design Result: Mixture (No Intercept) - Add to Design Tab
Optimal Design Result: Mixture (No Intercept) - Quick Tab
Select Factors for 2D Scatterplot
Select Factors for 3D Scatterplot
Optimal Design Result: Mixture (No Intercept) - Confounding Tab
Optimal Design Result: Response Surface
Optimal Design Result: Response Surface - Display Design Tab
Optimal Design Result: Response Surface - Add to Design Tab
Optimal Design Result: Response Surface - Quick Tab
Optimal Design Result: Response Surface - Confounding Tab
Efficiency Measures for D- and A- Optimal Designs
D-Optimal Split Designs Overview
Optimal Split Plot Overview
Optimal Split Plot Design
Optimal Split Plot Design - Quick Tab
Optimal Split Plot Design - Advanced Tab
Optimal Split Plot Design - Model Tab
Optimal Split Plot Design Results
Optimal Split Plot Design Results - Display Design Tab
Optimal Split Plot Design Results - Add to Design Tab
Optimal Split Plot Design Results - Design Details Tab
Designs for Constrained Surfaces and Mixtures
Design Points (Vertices & Centroids) for Constrained Surfaces and Mixtures
Design Points (Vertices & Centroids) for Constrained Surfaces and Mixtures - Options Tab
Design Points (Vertices & Centroids) for Constrained Surfaces and Mixtures - Quick Tab
Initial High/Low Constraints for Factors
Enter the Parameters of the General Linear Constraint
Constrained Mixture Design Results
Constrained Mixture Design Results - Display Design Tab
Constrained Mixture Design Results - Add to Design Tab
Constrained Mixture Design Results - Confounding Tab
Constrained Mixture Design Results - Quick Tab
Select Factors for 3D Scatterplot
Constrained Mixture Design Results - Review Constraints Tab
Constrained Surface Design Results
Constrained Surface Design Results - Display Design Tab
Constrained Surface Design Results - Add to Design Tab
Constrained Surface Design Results - Confounding Tab
Constrained Surface Design Results - Quick Tab
Select Factors for 3D Scatterplot
Constrained Surface Design Results - Review Constraints Tab
Experimental Design Builder and Quick Tab
Experimental Design Builder - Design Options Tab
Experimental Design Builder Results and Quick Tab
Experimental Design Builder Results - Display Design Tab
Experimental Design Builder Results - Add to Design Tab
Experimental Design Builder Results - Design Summary Tab
Experimental Design Builder Results - Prediction Variance Tab
Latin Square Designs
Design & Analysis of Latin Square Experiments
Design & Analysis of Latin Square Experiments - Analyze Design Tab
Design & Analysis of Latin Square Experiments - Design Experiment Tab
Latin Squares - Select 1 Latin Square
Latin Squares - Select 2 Latin Squares
Latin Squares - Select 3 Latin Squares
Design of a Latin Square Experiment
Design of a Latin Square Experiment - Quick Tab
Design of a Latin Square Experiment - Display Design Tab
Design of a Latin Square Experiment - Add to Design Tab
Analysis of a Latin Square Experiment
Analysis of a Latin Square Experiment - Quick Tab
Analysis of a Latin Square Experiment - Design Tab
Analysis of a Latin Square Experiment - ANOVA Tab
Analysis of a Latin Square Experiment - Means Tab
Mixed 2 and 3 Level Designs
Design & Analysis of Experiments with Two- and Three-Level Factors
Design & Analysis of Experiments with Two- and Three-Level Factors - Design Experiment Tab
Design & Analysis of Experiments with Two- and Three-Level Factors - Analyze Design Tab
Design of an Experiment with Two- and Three-Level Factors
Design of an Experiment with Two- and Three-Level Factors - Quick Tab
Design of a 2 and/or 3-level Screening Experiment - Display Design Tab
Design of an Experiment with Two- and Three-Level Factors - Add to Design Tab
Design of an Experiment with Two- and Three-Level Factors - Generators and Aliases Tab
Analysis of an Experiment with Two- and Three-Level Factors
Analysis of an Experiment with Two- and Three-Level Factors - Design Tab
Analysis of an Experiment with Two- and Three-Level Factors - ANOVA/Effects Tab
Analysis of an Experiment with Two- and Three-Level Factors - Review/Save Residuals Tab
Analysis of an Experiment with Two- and Three-Level Factors - Residual Plots Tab
Analysis of an Experiment with Two- and Three-Level Factors - Box-Cox Tab
Analysis of an Experiment with Two- and Three-Level Factors - Quick Tab
Compute Marginal Means for
Specify the Arrangement of the Factors in the Plot
Analysis of an Experiment with Two- and Three-Level Factors - Model Tab
Customized (Pooled) Error Term
Analysis of an Experiment with Two- and Three-Level Factors - Means Tab
Compute Marginal Means for
Specify the Arrangement of the Factors in the Plot
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Analysis of an Experiment with Two- and Three-Level Factors - Prediction and Profiling Tab
Select Factors for 3D Plot
Select Factor Values (When Predicting Dependent Variable Values)
Select Factor Values (When Creating Surface or Contour Plots).
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
Main Effects and Interactions for Experiments with Two- and Three-Level Factors
ANOVA Tables for Experiments with Two- and Three-Level Factors
Experimental Design - Critical Values for Experiments with Two- and Three-Level Factors
Marginal Means in Experiments with Two- and Three-Level Factors
Mixture Designs and Triangular Surfaces
Design & Analysis of Mixture Experiments
Design & Analysis of Mixture Experiments - Design Experiment Tab
Design & Analysis of Mixture Experiments - Analyze Design Tab
Specifications for Mixture Variables
Design of a Mixture Experiment
Design of a Mixture Experiment - Quick Tab
Design of a Mixture Experiment - Display Design Tab
Design of a Mixture Experiment - Add to Design Tab
Analysis of a Mixture Experiment
Analysis of a Mixture Experiment - Display Design Tab
Analysis of a Mixture Experiment - ANOVA/Effects Tab
Analysis of a Mixture Experiment - Review/Save Residuals Tab
Analysis of a Mixture Experiment - Residual Plots Tab
Analysis of a Mixture Experiment - Box-Cox Tab
Analysis of a Mixture Experiment - Quick Tab
Experimental Design - Values for Reference Blend
Analysis of a Mixture Experiment - Model Tab
Customized (Pooled) Error Term
Analysis of a Mixture Experiment - Prediction & Profiling Tab
Experimental Design - Select Factors for Triangular Graph
Experimental Design - Values for Reference Blend
Experimental Design - Profiler
Profiler - Save/Open Tab
Profiler - Options Tab
Profiler - Quick Tab
Profiler - Select Factor Values
Profiler - Specifications for Factor Grid
ANOVA Tables for Mixture Experiments
Estimates, Pseudo-Components in Mixture Experiments
Experimental Design - Trace Plots for Mixture Experiments
Experimental Design - Critical Values for Mixture Experiments
Six Sigma Calculator
Taguchi Methods: Robust Design Experiments
Design & Analysis of Taguchi Robust Design Experiments
Design & Analysis of Taguchi Robust Design Experiments - Design Experiment Tab
Design & Analysis of Taguchi Robust Design Experiments - Analyze Design Tab
Design of a Robust Design Experiment
Design of a Robust Design Experiment - Quick Tab
Design of a Robust Design Experiment - Display Design Tab
Design of a Robust Design Experiment - Add to Design Tab
Design of a Robust Design Experiment - Alias Structure Tab
Analysis of a Robust Design Experiment
Analysis of a Robust Design Experiment - Quick Tab
Analysis of a Robust Design Experiment - Design Tab
Analysis of a Robust Design Experiment - ANOVA Tab
Analysis of a Robust Design Experiment - Means Tab
Analysis of a Robust Design Experiment  and Accumulation Analysis Results - Optimum Eta Tab
Experimental Design - Select 2 Factors for Interaction
Experimental Design - Select Effects to be Pooled
Experimental Design - User-Defined Factor Settings
Accumulation Analysis Results
Accumulation Analysis Results - Quick Tab
Accumulation Analysis Results - Design Tab
Accumulation Analysis Results - Means Tab
Accumulation Analysis Results - Optimum Eta Tab
Experimental Design - Select Effects to be Pooled
Experimental Design - User-Defined Factor Settings
Experimental Design Examples
Example 1.1: Designing and Analyzing a 2(7-4) Fractional Factorial Design
Example 1.2: Analyzing a 26 Full Factorial
Example 1.3: Analyzing a Botched 2(7-4) Fractional Factorial Design
Example 2: Designing and Analyzing a 35-Factor Screening Design
Example 3: Analyzing a 33 Full Factorial
Example 4: Designing and Analyzing a 2332 Experiment
Example 5: Central Composite (Response Surface) Designs
Example 6: Latin Square Designs
Example 7: Taguchi Robust Design Experiment
Example 8.1: Designing and Analyzing a Mixture Experiment
Example 8.2: Designing and Analyzing a Mixture Experiment with Pseudo-Components
Example 9.1: Finding Vertex and Centroid Points for a Constrained Region
Example 9.2: Mixture Designs for Components with Upper and Lower Bound Restrictions
Example 10.1: Constructing a Simple D-Optimal Design
Example 10.2: Constructing a Design from Vertex and Centroid Points (Example 9.1, Continued)
Example 11: Constructing a D-Optimal Split Plot Design
Example 12: Getting Started Constructing a D-Optimal Design
Example 13: Constructing an I-Optimal Design
Bayesian Reliability Optimization for Continuous Response Example
Special Topics Example 1 - Simultaneous Optimization of Several Response Variables in a Central Composite (Response Surface) Design
Special Topics Example 2 - Optimization of the Response Variable in a Three-Factor Mixture Experiment
Special Topics Example 3 - Residuals Analysis
Special Topics Example 4 - Box-Cox Transformation of a Dependent Variable
Experimental Design - Box-Cox Plots for Selecting Transformations
Denoting Factors in Experimental Design
Experimental Design - Summary for Variables (Factors)
Experimental Design - Notes and Technical Information
Experimental Design - Note on Missing Data and Multiple Dependent Variables
Experimental Design - Note on Unbalanced Designs and Singular Effects Matrices
How to Read the Aliases of Main Effects & Interactions Spreadsheet
Special Topics - Box-Cox Transformations of Dependent Variables
Special Topics - Profiling Predicted Responses and Response Desirability
Special Topics - Residuals Analysis
Special Topics in Experimental Design
Mutivariate Quality Control
Multivariate Quality Control Charts Overview
The Architecture of the Multivariate Quality Control Charts Module
General Purpose
General Approach
Multivariate Quality Control Computational Details
Establishing Control Limits
Common Types of Multivariate Control Charts
Multivariate Quality Control Charts Events
Measurements Related to Product Quality: Custom Alarm Handling and Custom SVB
Types of Multivariate Quality Control Charts Analyses
Defining Variables for Hotelling T-Square (Individuals) Chart
Defining Variables for Hotelling T-Square (Individuals) Chart - Quick Tab
Defining Variables for Hotelling T-Square (Individuals) Chart - Sets Tab
Defining Variables for Hotelling T-Square (Individuals) Chart - Specs Tab
Defining Variables for Hotelling T-Square (Individuals) Chart - Labels, Causes, and Actions Tab
Defining Variables for Hotelling T-Square (Means)/GV Chart
Defining Variables for Hotelling T-Square (Means)/GV Chart - Quick Tab
Defining Variables for Hotelling T-Square (Means)/GV Chart - Sets Tab
Defining Variables for Hotelling T-Square (Means)/GV Chart - Specs Tab
Defining Variables for Hotelling T-Square (Means)/GV - Labels, Causes, and Actions Tab
Defining Variables for MEWMA (Individuals) Chart
Defining Variables for MEWMA (Individuals) Chart - Quick Tab
Defining Variables for MEWMA (Individuals) Chart - Sets Tab
Defining Variables for MEWMA (Individuals) Chart - Specs Tab
Defining Variables for MEWMA (Individuals) Chart - Labels, Causes, and Actions Tab
Defining Variables for MEWMA (Means) Chart
Defining Variables for MEWMA (Means) Chart - Quick Tab
Defining Variables for MEWMA (Means) Chart - Sets Tab
Defining Variables for MEWMA (Means) Chart - Specs Tab
Defining Variables for MEWMA (Means) Chart - Labels, Causes, and Actions Tab
Defining Variables for Multivariate CUSUM Chart
Defining Variables for Multivariate CUSUM Chart - Quick Tab
Defining Variables for Multivariate CUSUM Chart - Sets Tab
Defining Variables for Multivariate CUSUM Chart - Specs Tab
Defining Variables for Multivariate CUSUM Chart - Labels, Causes, and Actions Tab
Defining Variables for Multiple Stream X and MR Chart
Defining Variables for Multiple Stream X and MR Chart - Quick Tab
Defining Variables for Multiple Stream X and MR Chart - Sets Tab
Defining Variables for Multiple Stream X and MR Chart - Labels, Causes, and Actions Tab
Defining Variables for Multiple Stream X-Bar and R Chart
Defining Variables for Multiple Stream X-Bar and R Chart - Quick Tab
Defining Variables for Multiple Stream X-Bar and R Chart - Sets Tab
Defining Variables for Multiple Stream X-Bar and R Chart - Labels, Causes, and Actions Tab
Defining Variables for Multiple Stream X-Bar and S Chart
Defining Variables for Multiple Stream X-Bar and S Chart - Quick Tab
Defining Variables for Multiple Stream X-Bar and S Chart - Sets Tab
Defining Variables for Multiple Stream X-Bar and S Chart - Labels, Cause, Actions Tab
Hotelling T2, MEWMA, MCUSUM, and Multiple Stream Results Dialog Boxes
Hotelling T2, MEWMA, MCUSUM, and Multiple Stream Results Dialog Boxes - Charts Tab
Hotelling T2, MEWMA, and MCUSUM Results Dialogs - X(Multivariate) Tab; Multiple Stream Results Dialogs - X(XBar) Specs Tab
Hotelling T2 for Means, and Multiple Stream Charts Results Dialogs - GV Specs/R/S Specs Tab
Hotelling T2, MEWMA, MCUSUM, and Multiple Stream Results Dialog Boxes - Sets Tab
Hotelling T2, MEWMA, MCUSUM, and Multiple Stream Results Dialog Boxes - Brushing Tab
Multivariate Quality Control Charts Startup Panel
Multivariate Quality Control Charts Startup Panel - Quick Tab
Multivariate Quality Control Charts Startup Panel - Real-Time Tab
Options
Options Dialog - Alarms Tab
Options Dialog - Labeling Tab
Options Dialog - Layout Tab
Options Dialog - Patterns Tab
Options Dialog - Scaling Tab
MQC Charts Examples
MQC Charts Example 1 - Creating a Hotelling's T2 Chart
MQC Charts Example 2 - Creating a MEWMA Chart for Individuals
MQC Charts Example 3 - Computing Chart Parameters for Different Sets
MQC Charts Example 4 - Creating a Multiple Stream Chart
MQC Charts Example 5 - Creating a Multivariate CUSUM Chart
Six Sigma (DMAIC) Shortcuts
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Define - Cause and Effect Diagrams
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Define - Blank Graph
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Define - Six Sigma Calculator
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Descriptive Statistics
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - SixGraph Summary
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Missing and Out of Range Data
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Box and Whisker Plot
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Gage R & R
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Sampling
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Power Analysis
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Process Capability
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Distribution Fitting
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Frequency Tables
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Measure - Time Series Plots
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - Cause and Effect Diagrams
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - t-test
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - Paired t-test
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - Nonparametric Tests
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - Stratification Analysis
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - Correlations
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - 2D Scatterplots
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - 3D Scatterplots
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - Regression
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - Experimental Design
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - GLM
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Analyze - GLZ
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - Quality Control Charts
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - SixGraph Summary
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - Frequency
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - Histogram
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - Pareto Charts
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - Process Capability Charts
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - Experimental Design
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - Six Sigma Calculator
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - Stratification Analysis
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Improve - Stratification Charts
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Control - Quality Control Charts
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Display Six Sigma Menu
Statistics - Industrial Statistics And Six Sigma - Six Sigma (DMAIC) Shortcuts - Customize - Display Six Sigma Toolbar
Power Analysis
Power Analysis Overview
Power Analysis Introductory Overview - Power Analysis and Sample Size Calculation in Experimental Design
Power Analysis and Sample Size Calculation in Experimental Design - Sampling Theory and Hypothesis Testing Logic
Power Analysis and Sample Size Calculation in Experimental Design - Calculating Power
Power Analysis and Sample Size Calculation in Experimental Design - Calculating Required Sample Size
Power Analysis and Sample Size Calculation in Experimental Design - Graphical Approaches to Power Analysis
Power Analysis Introductory Overview - Noncentrality Interval Estimation and the Evaluation of Statistical Models
Noncentrality Interval Estimation and the Evaluation of Statistical Models - Advantages of Interval Estimation
Noncentrality Interval Estimation and the Evaluation of Statistical Models - Inadequacies of the Hypothesis Testing Approach
Noncentrality Interval Estimation and the Evaluation of Statistical Models - Reasons Why Interval Estimates are Seldom Reported
Noncentrality Interval Estimation and the Evaluation of Statistical Models - Replacing Traditional Hypothesis Tests with Interval Estimates
Power Analysis and Interval Estimation Startup Panel and Quick Tab
1-Sample t-test: Power Calculation Parameters
Power Analysis and Interval Estimation - Settings I/O Tab
1 Sample t-Test: Power Calculation Results
1 Sample t-Test: Power Calculation Results - Quick Tab
Independent Sample t-Test: Power Calculation Parameters
Independent Sample t-Test: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Independent Sample t-Test: Power Calculation Results
Independent Sample t-Test: Power Calculation Results - Quick Tab
Dependent Sample t-Test: Power Calculation Parameters
Dependent Sample t-Test: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Dependent Sample t-Test: Power Calculation Results
Dependent Sample t-Test: Power Calculation Results - Quick Tab
Planned Contrast: Power Calculation Parameters
Planned Contrast: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Planned Contrast: Power Calculation Results
Planned Contrast: Power Calculation Results - Quick Tab
1-Way ANOVA: Power Calculation Parameters
1-Way ANOVA: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
1-Way ANOVA: Power Calculation Results
1-Way ANOVA: Power Calculation Results - Quick Tab
2-Way ANOVA: Power Calculation Parameters
2-Way ANOVA: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
2-Way ANOVA: Power Calculation Results
2-Way ANOVA: Power Calculation Results - Quick Tab
One Variance: Power Calculation Parameters
One Variance: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
One Variance: Power Calculation Results
One Variance: Power Calculation Results - Quick Tab
Two Variances: Power Calculation Parameters
Two Variances: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Two Variances: Power Calculation Results
Two Variances: Power Calculation Results - Quick Tab
t-Test for Zero Correlation: Power Calculation Parameters
t-Test for Zero Correlation: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
t-Test for Zero Correlation: Power Calculation Results
t-Test for Zero Correlation: Power Calculation Results - Quick Tab
Two Correlations: Power Calculation Parameters
Two Correlations: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Two Correlations: Power Calculation Results
Two Correlations: Power Calculation Results - Quick Tab
Multiple R²: Power Calculation Parameters
Multiple R²: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Multiple R²: Power Calculation Results
Multiple R²: Power Calculation Results - Quick Tab
Single Proportion: Power Calculation Parameters
Single Proportion: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Single Proportion: Power Calculation Results
Single Proportion: Power Calculation Results - Quick Tab
Comparing 2 Proportions: Power Calculation Parameters
Comparing 2 Proportions: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Comparing 2 Proportions: Power Calculation Results
Comparing 2 Proportions: Power Calculation Results - Quick Tab
McNemar's Test: Power Calculation Parameters
McNemar's Test: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
McNemar's Test: Power Calculation Results
McNemar's Test: Power Calculation Results - Quick Tab
Log-Rank Test: Power Calculation Parameters
Log-Rank Test: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Log-Rank Test: Power Calculation Results
Log-Rank Test: Power Calculation Results - Quick Tab
Survival, Exponential, Accrual: Power Calculation Parameters
Survival, Exponential, Accrual: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Survival, Exponential, Accrual: Power Calculation Results
Survival, Exponential, Accrual: Power Calculation Results - Quick Tab
Survival, Exponential, Accrual, Dropouts: Power Calculation Parameters
Survival, Exponential, Accrual, Dropouts: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Survival, Exponential, Accrual, Dropouts: Power Calculation Results
Survival, Exponential, Accrual, Dropouts: Power Calculation Results - Quick Tab
Structural Equation Modeling: Power Calculation Parameters
Structural Equation Modeling: Power Calculation Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Structural Equation Modeling: Power Calculation Results
Structural Equation Modeling: Power Calculation Results - Quick Tab
Sample Size Calculation
One Mean t-Test
1 Sample t-Test: Sample Size Parameters
1 Sample t-Test: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
1 Sample t-Test: Sample Size Calculation Results
1 Sample t-Test: Sample Size Calculation Results - Quick Tab
Two Means, t-Test, Ind. Samples
Independent Sample t-Test: Sample Size Parameters
Independent Sample t-Test: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Independent Sample t-Test: Sample Size Calculation Results
Independent Sample t-Test: Sample Size Calculation Results - Quick Tab
Two Means, t-Test Dep. Samples
Dependent Sample t-Test: Sample Size Parameters
Dependent Sample t-Test: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Dependent Sample t-Test: Sample Size Calculation Results
Dependent Sample t-Test: Sample Size Calculation Results - Quick Tab
Several Means, Planned Contrast
Planned Contrast: Sample Size Parameters
Planned Contrast: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Planned Contrast: Sample Size Calculation Results
Planned Contrast: Sample Size Calculation Results - Quick Tab
Several Means, ANOVA, 1-Way
1-Way ANOVA: Sample Size Parameters
Power Analysis and Interval Estimation - Settings I/O Tab
1-Way ANOVA: Sample Size Calculation Results
1-Way ANOVA: Sample Size Calculation Results - Quick Tab
1-Way ANOVA: Sample Size Parameters - Quick Tab
ANOVA Effects Calculation
Several Means, ANOVA, 2-Way
2-Way ANOVA: Sample Size Parameters
Power Analysis and Interval Estimation - Settings I/O Tab
2-Way ANOVA: Sample Size Calculation Results
2-Way ANOVA: Sample Size Calculation Results - Quick Tab
2-Way ANOVA: Sample Size Parameters - Quick Tab
2-Way ANOVA Effects Calculation
One Variance, Chi-Square Test
One Variance: Sample Size Parameters
One Variance: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
One Variance: Sample Size Calculation Results
One Variance: Sample Size Calculation Results - Quick Tab
Two Variances, F-Test
Two Variances: Sample Size Parameters
Two Variances: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Two Variances: Sample Size Calculation Results
Two Variances: Sample Size Calculation Results - Quick Tab
One Correlation, t-Test
t-Test for Zero Correlation: Sample Size Parameters
t-Test for Zero Correlation: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
t-Test for Zero Correlation: Sample Size Calculation Results
t-Test for Zero Correlation: Sample Size Calculation Results - Quick Tab
Two Correlations, Z-Test
Two Correlations: Sample Size Parameters
Two Correlations: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Two Correlations: Sample Size Calculation Results
Two Correlations: Sample Size Calculation Results - Quick Tab
Squared Multiple Correlation
Multiple R²: Sample Size Parameters
Multiple R²: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Multiple R²: Sample Size Calculation Results
Multiple R²: Sample Size Calculation Results - Quick Tab
One Proportion, Z, Chi-Square Test
Single Proportion: Sample Size Parameters
Single Proportion: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Single Proportion: Sample Size Calculation Results
Single Proportion: Sample Size Calculation Results - Quick Tab
Two Proportions, Z-Test
Comparing 2 Proportions: Sample Size Parameters
Comparing 2 Proportions: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Comparing 2 Proportions: Sample Size Calculation Results
Comparing 2 Proportions: Sample Size Calculation Results - Quick Tab
Two Proportions, Paired Sample
McNemar's Test: Sample Size Parameters
McNemar's Test: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
McNemar's Test: Sample Size Calculation Results
McNemar's Test: Sample Size Calculation Results - Quick Tab
Survival - Log-Rank Test
Log-Rank Test: Sample Size Parameters
Log-Rank Test: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Log-Rank Test: Sample Size Calculation Results
Log-Rank Test: Sample Size Calculation Results - Quick Tab
Survival - Exponential, Accrual
Survival, Exponential, Accrual: Sample Size Parameters
Survival, Exponential, Accrual: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Survival, Exponential, Accrual: Sample Size Calculation Results
Survival, Exponential, Accrual: Sample Size Calculation Results - Quick Tab
Survival - Exp., Accrual/Dropouts
Survival, Exponential, Accrual, Dropouts: Sample Size Parameters
Survival, Exponential, Accrual, Dropouts: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Survival, Exponential, Accrual, Dropouts: Sample Size Calculation Results
Survival, Exponential, Accrual, Dropouts: Sample Size Calculation Results - Quick Tab
Structural Equation Modeling
Structural Equation Modeling: Sample Size Parameters
Structural Equation Modeling: Sample Size Parameters - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Structural Equation Modeling: Sample Size Calculation Results
Structural Equation Modeling: Sample Size Calculation Results - Quick Tab
Interval Estimation
1 Sample t-Test: Interval Estimation
1 Sample t-Test: Interval Estimation - Quick Tab
Power Analysis and Interval Estimation - Settings I/O Tab
Independent Sample t-Test: Interval Estimation
Independent Sample t-Test: Interval Estimation - Quick Tab
Dependent Sample t-Test: Interval Estimation
Dependent Sample t-Test: Interval Estimation - Quick Tab
Planned Contrast: Interval Estimation
Planned Contrast: Interval Estimation - Quick Tab
Planned Contrast: Effects Calculation
1-Way ANOVA: Interval Estimation
1-Way ANOVA: Interval Estimation - Quick Tab
2-Way ANOVA: Interval Estimation
2-Way ANOVA: Interval Estimation - Quick Tab
t-Test for Zero Correlation: Interval Estimation
t-Test for Zero Correlation: Interval Estimation - Quick Tab
Multiple R²: Interval Estimation
Multiple R²: Interval Estimation: Quick Tab
Single Proportion: Interval Estimation
Single Proportion: Interval Estimation - Quick Tab
Structural Equation Modeling: Interval Estimation
Structural Equation Modeling: Interval Estimation - Quick Tab
Probability Distributions
Binomial Distribution Probability Calculator
Binomial Distribution Probability Calculator - Quick Tab
Multiple R² Probability Calculator
Multiple R² Probability Calculator - Quick Tab
Noncentral Chi-square Probability Calculator
Noncentral Chi-square Probability Calculator - Quick Tab
Pearson Correlation Probability Calculator
Pearson Correlation Probability Calculator - Quick Tab
Noncentral t Probability Calculator
Noncentral t Probability Calculator - Quick Tab
Noncentral F Probability Calculator
Noncentral F Probability Calculator - Quick Tab
Examples
Example 1: Power and Sample Size Calculation for the Independent Sample t-Test
Example 2: Analyzing Power, Sample Size, and Effect Size in 1-Way ANOVA
Example 3: Sample Size Calculation in Factor Analysis
Example 4: Power and Sample Size in Complex Factorial ANOVA
Example 5: Power Calculation in a 1-Way Repeated Measures ANOVA
Example 6: Hypotheses About the Noncentrality Parameter of the F Distribution
Example 7: Constructing a Confidence Interval on the Noncentrality Parameter
Example 8: Power of Nonstandard Significance Tests in the Analysis of Variance
Example 9: Exact Tests and Confidence Intervals for the Correlation Coefficient
Example 10: Confidence Intervals and Special Tests on the Multiple Correlation
Statistica Automated Neural Networks (SANN) - Neural Networks Overview
SANN Overviews - Neural Network Tasks
SANN Overviews - Network Types
SANN Overviews - Activation Functions
SANN Overviews - Selecting the Input Variables
SANN Overviews - Neural Network Complexity
SANN Overviews - Network Training
SANN Overviews - Network Generalization
SANN Overviews - Pre and Post Processing of Data
SANN Overviews - Predicting Future Data and Deployment
SANN Overviews - Recommended Textbooks
SANN Overviews - Ensembles and Subsampling
SANN - New Analysis/Deployment Startup Panel and Quick Tab
SANN - Data Selection
SANN - Data Selection - Quick Tab
SANN - Data Selection - Sampling (CNN and ANS) Tab
SANN - Data Selection - Subsampling Tab
SANN - Data Selection - Time Series Tab
SANN - Automated Network Search (ANS)
SANN - Automated Network Search (ANS) - Quick Tab
SANN - Automated Network Search (ANS) - MLP Activation Functions Tab
SANN - Automated Network Search (ANS) - Weight Decay Tab
SANN - Automated Network Search (ANS)  Initialization Tab
Neural Network Training in Progress
SANN - Custom Neural Network
SANN - Custom Neural Network - Quick Tab
SANN - Custom Neural Network/Subsampling - MLP Tab
SANN - Custom Neural Network/Subsampling - RBF Tab
SANN - Custom Neural Network/Subsampling - Weight Decay Tab
SANN - Custom Neural Network/Automated Network Search (ANS)/Subsampling - Initialization Tab
SANN - Custom Neural Network/Subsampling - Real Time Training Graph Tab
SANN - Custom Neural Network - Quick (Kohonen) Tab
SANN - Custom Neural Network - Kohonen Training Tab
SANN - Subsampling Dialog Box and Quick Tab
SANN - Subsampling - MLP Tab
SANN - Subsampling - RBF Tab
SANN - Subsampling – Weight Decay Tab
SANN - Subsampling - Initialization Tab
SANN - Custom Neural Network/Subsampling - Real Time Training Graph Tab
SANN - Results
SANN - Results - Predictions Tab
SANN - Results - Graphs Tab
SANN - Results - Details Tab
SANN - Results - Custom Predictions Tab
SANN - Results - Predictions (Kohonen) Tab
SANN - Results - Graphs Tab
SANN - Results - Kohonen Graph Tab
SANN - Results - Liftcharts Tab
SANN - Results - Time Series Tab
Model Activation
Model Deletion
Examples
SANN Example 1: Performing Regression with 4-Bar Linkage Data
SANN Example 2: The Iris Problem (Classification)
SANN Example 3: Growth in Number of Airline Passengers over Time
SANN Example 4: Time Series (Classification)
SANN Example 5: Cluster Analysis in SANN
SANN Example 6: Deploying the 4Bar Linkage Data
PLS, PCA, Multivariate/Batch SPC
Multivariate Statistical Process Control (MSPC) and Nonlinear Iterative Partial Least Squares (NIPALS) Overview
Multivariate Statistical Process Control and Quick Tab
Deployment
Deployment Model - Quick Tab
Deployment Model - Results Tab
Deployment Model
PCA
PCA
PCA - Fitting Tab
PCA - NIPALS Tab
PCA - Quick Tab
PCA Results - Quick Tab
PCA - Options Tab
PCA Results - Advanced Tab
PCA Results - Quality Tab
PCA Results
PCA Results - MD Tab
PCA - Advanced Tab
PCA Results - Plots Tab
PCA Example
PCA Deployment Example
Variable Block
Biplot Variables
PLS
PLS - Fitting Tab
PLS - Quick Tab
PLS - Options Tab
PLS - NIPALS Tab
PLS
PLS Results
PLS Results - Plots Tab
PLS - Advanced Tab
PLS Results - Quick Tab
PLS Results - MD Tab
PLS Results - Advanced Tab
PLS Results - Quality Tab
User-Defined Scale (Standard Deviation)
Test Sample Identifier
TMPCA
TMPCA - NIPALS Tab
TMPCA - Advanced Tab
TMPCA - Quick Tab
TMPCA
TMPCA - Fitting Tab
TMPCA Results - Quick Tab
TMPCA Results
TMPCA Results - Advanced Tab
TMPCA Results - Plots Tab
TMPCA Example
Select Batch Codes
BMPLS
BMPLS - Advanced Tab
BMPLS - NIPALS Tab
BMPLS Results
BMPLS
BMPLS Results - Plots Tab
BMPLS Results - Advanced Tab
BMPLS - Fitting Tab
BMPLS - Quick Tab
BMPLS Results - Quick Tab
TMPLS
TMPLS - Quick Tab
TMPLS Results - Quick Tab
TMPLS - Advanced Tab
TMPLS Results
TMPLS - Fitting Tab
TMPLS Results - Plots Tab
TMPLS - NIPALS Tab
TMPLS Results - Advanced Tab
TMPLS
PCA/PLS Startup Panel and Quick Tab
MSPC Raw Data Example
Multivariate Statistical Process Control (MSPC) Technical Notes
Principal Component Analysis (PCA) and Partial Least Squares (PLS) Technical Notes
NIPALS Technical Notes
Brushing Commands
Overviews
VEPAC Overview
Overview - Fixed and Random Effects
Overview - ANOVA and REML Estimation Methods
Overview - ANOVA and REML Method Implementation in Variance Estimation and Precision
Computational Details
Technical Notes - Determining the Default Design
Technical Notes - ByGroup Analysis with Variance Estimation and Precision
Dialogs
 Variance Estimation and Precision Startup Panel
Define/Review Model
Define Custom Design
Variance Estimation and Precision Results
Variance Estimation and Precision Results - Summary Tab
Collapse Variance Components
Collapsed Name
Variance Estimation and Precision Results - Residuals Tab
Variance Estimation and Precision Results - Variance Evaluation Tab
Variance Estimation and Precision Results - Means Comparisons Tab
Select an Effect
Examples
Example 1: Balanced and Unbalanced Two-Way Mixed Models
Example 2: Variance Component Estimation for a Four-Way Mixed Factorial Design
Example 3: Variance Component Estimation for a Hierarchically Nested Random Design
Example 4: Estimating the Population Intraclass Correlation in Variance Estimation and Precision
Example 5: Analyzing a Nested Mixed Model
Statistics of Block Data
Block Columns - Rows
Statistics - Statistics of Block Data - Block Columns/Rows - Means
Statistics - Statistics of Block Data - Block Columns/Rows - Medians
Statistics - Statistics of Block Data - Block Columns/Rows - SD's
Statistics - Statistics of Block Data - Block Columns/Rows - Valid N's
Statistics - Statistics of Block Data - Block Columns/Rows - Sums
Statistics - Statistics of Block Data - Block Columns/Rows - Min's
Statistics - Statistics of Block Data - Block Columns/Rows - Max's
Statistics - Statistics of Block Data - Block Columns/Rows - 25%'s
Statistics - Statistics of Block Data - Block Columns/Rows - 75%'s
Statistics - Statistics of Block Data - Block Columns/Rows - All
Statistica Visual Basic
UnderstandingMacros
Macro (Statistica Visual Basic) Overview
Macro (SVB) Programs Window Overview
Debugging/Logging
Optimization
Speeding Up the Execution (Loading) of Statistica Visual Basic Programs by Removing Unnecessary References (Type Libraries)
MatrixFunctions
MatrixandStatisticalFunctionLibraries
Statistica Matrix and Statistical Function Libraries
Include File Graphics.svx
Include File STB.svx and Statistica Visual Basic Library of Matrix Functions
A Simple Example - Inverting a Matrix
Example - Accessing Variables (Columns) in Spreadsheets via the .VData(i) Property
Example - Accessing All Data in Spreadsheets via the .Data Property
Accessing the Matrix Library via the MatrixObject
Statistica Visual Basic Library of Matrix Functions - MatrixFill (Scalar, DestMatrix, DestRow, DestCol, ?NoOfRows, ?NoOfCols)
Statistica Visual Basic Library of Matrix Functions - MatrixSingularValuesDecomp (Matrix, U, W, V)
Statistica Visual Basic Library of Matrix Functions - VectorDualSort (Vector, IndexVector, Direction)
Statistica Visual Basic Library of Matrix Functions - MatrixSubtract (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixTranspose (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixSumOfSquares (Matrix, Result)
Statistica Visual Basic Library of Matrix Functions - MatrixSolve (Matrix, MatrixSolution, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixTrace (Matrix, Result)
Statistica Visual Basic Library of Matrix Functions - MatrixSetRow (Matrix, Row, RowVector)
Statistica Visual Basic Library of Matrix Functions - MatrixWrite (FileName$, Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixSetToZero (Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixSweep (Matrix, From, To, Dir)
Statistica Visual Basic Library of Matrix Functions - ReDim Array_Name (XDim, ...)
Statistica Visual Basic Library of Matrix Functions - MatrixSetDiagonal (Matrix, DiagVector)
Statistica Visual Basic Library of Matrix Functions - NewScrollsheet (?NoOfRows, ?NoOfColumns, Array, ?Title$, ?RowNames$, ?ColumnNames)
Statistica Visual Basic Library of Matrix Functions - MatrixReadFromDataFile (FileName$, ?FirstCase, ?FirstVar, ?NoOfCases, ?NoOfVars, Matrix)
Statistica Visual Basic Library of Matrix Functions - Wishart
Statistica Visual Basic Library of Matrix Functions - VectorSort (Vector, Direction)
Statistica Visual Basic Library of Matrix Functions - MatrixSetColumn (Matrix, Column, ColVector)
Statistica Visual Basic Library of Matrix Functions - MatrixMeans (Matrix, Vector)
Statistica Visual Basic Library of Matrix Functions - MatrixDet (Matrix, Result)
Statistica Visual Basic Library of Matrix Functions - MatrixElemMultiply (Matrix, Scalar, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixGetDiagonal (Matrix, DiagVector)
Statistica Visual Basic Library of Matrix Functions - MatrixElemAdd (Matrix, Scalar, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixEigenValues (Matrix, Eigenvalues, NumEigen)
Statistica Visual Basic Library of Matrix Functions - MatrixElemSubtract (Matrix, Scalar, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixGetColumn (Matrix, Column, ColVector)
Statistica Visual Basic Library of Matrix Functions - MatrixDisplay (Matrix, ?Title$)
Statistica Visual Basic Library of Matrix Functions - MatrixRank (Matrix, Result)
Statistica Visual Basic Library of Matrix Functions - MatrixRead (FileName$, Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixSingularValues (Matrix, SingularValues)
Statistica Visual Basic Library of Matrix Functions - MatrixIsGreaterEqual (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixKroneckerMultiply (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsLess (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixMultiply (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixLogicalNot (Matrix1, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsGreater (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIdentity (Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixIsNotEqual (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsLessEqual (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixLogicalOr (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixLogicalAnd (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixInverse (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsEqual (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - ScrollsheetGetMatrix (Handle, Row, Column, Matrix)
Statistica Visual Basic Library of Matrix Functions - Lowess (VectorX, VectorY, SmoothF, Nsteps, Delta, VectorXSorted, VectorYSmooth, VectorRWeights, VectorResiduals)
Statistica Visual Basic Library of Matrix Functions - MatrixDuplicate (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - Dim Array_Name (XDim, ...)
Statistica Visual Basic Matrix Language - Overview
Statistica Visual Basic Library of Matrix Functions - MatrixAllNonZero (Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixCombineHoriz (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixGetRow (Matrix, Row, RowVector)
Statistica Visual Basic Library of Matrix Functions - MatrixGramSchmidtOrt (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixElemDivide (Matrix, Scalar, MatrixResult)
Statistica Visual Basic Library of Matrix Functions
Statistica Visual Basic Library of Matrix Functions - MatrixGeneralPseudolnverse (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixEigenVectors (Matrix, Eigenvalues, Eigenvectors, NumEigen)
Statistica Visual Basic Library of Matrix Functions - VectorRank (Vector, Direction, DupControl)
Statistica Visual Basic Library of Matrix Functions - ScrollsheetSetMatrix (Handle, Row, Column, Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixExtract (Matrix, Row, Column, ?NoOfRows, ?NoOfCols, SubMatrix)
Statistica Visual Basic Library of Matrix Functions - MatrixCopy (SrcMatrix, SrcRow, SrcCol, ?NoOfRows, ?NoOfCols, DestMatrix, DestRow, DestCol)
Statistica Visual Basic Library of Matrix Functions - MatrixCrossProductOfDev (Matrix1, Intercept, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixCorrelations (Matrix, Intercept, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixCholeskyDecomposition (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixAdd (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixAnyNonZero (Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixCombineVert (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - LOWESS (VectorX, VectorY, SmoothF, Nsteps, Delta, VectorXSorted, VectorYSmooth, VectorRWeights, VectorResiduals)
Examples
Macro (SVB) Program Examples
General Visual Basic Syntax Documentation - Interactive WinWrap Help Engine
Macro (SVB) Programs Example - Displaying a Progress Bar
Macro (SVB) Program Examples - Running a Query via a Macro
Macro (SVB) Program Examples - Creating a Statistica Query Toolbar
Macro (SVB) Programs Example - Inserting All Open Windows into a Workbook
Macro (SVB) Programs Example - Graph Function Library
Macro (SVB) Programs Example - Accessing and Customizing Graphs
Macro (SVB) Programs Example - Creating and Customizing Graph Objects
Macro (SVB) Programs Example - Basic Spreadsheet Manipulation
Macro (SVB) Program Examples - Creating a Random Sized Spreadsheet
Macro (SVB) Programs Example - Creating a Cell-Function Spreadsheet
Macro (SVB) Programs Example - Resize a Spreadsheet
Macro (SVB) Programs Example - Creating User-Defined Custom Functions
Macro (SVB) Programs Example - Creating Sample Statistics (Descriptives and Correlations)
Macro (SVB) Programs Example - Creating and Customizing Box-Plots
Macro (SVB) Programs Example - Inserting an Array into a Spreadsheet
Macro (SVB) Programs Example - Converting a Spreadsheet's Data Type
Macro SVB Programs Examples - Searching a Spreadsheet's Cells for a Value
Macro (SVB) Programs Example - Creating a Self-Updating Dataset
Macro (SVB) Programs Example - Auto-Fitting a Spreadsheet's Variable Columns
GeneralSVBConventions
Events
Document-Level Events Example
Document Events Window
Application-Level Events Example
Controlled Loops
Conditional Loops
How to use Strings in Statistica Visual Basic
Passing Lists of Strings to Statistica Visual Basic Functions
Statistica Visual Basic Syntax Reference - User-defined Functions and Subroutines
Basic Rules for Simple SVB Programs
Advanced Conditional Expressions
Variables and Values of Variables
Variable and Function Naming Conventions
Statistica Visual Basic Syntax Reference - User-defined Functions and Subroutines (Arguments)
Array Indexing
Redimensioning an Array
How to Specify Arrays in Statistica Visual Basic Functions
Declaring a Variable
Retrieving Lists of Strings in Statistica Visual Basic
Placing Results in Workbooks, Reports, Etc., via the RouteOutput Method
Importing Version 5.x STB Programs to Statistica Visual Basic (Technical Notes)
Importing Version 5.x SCL Programs to Statistica Visual Basic (Technical Notes)
QuickRef
How Do I Set (or Leave Unchanged) Default Values of Function Parameters?
Are There Example Statistica Visual Basic Programs Included in the Package?
How Do I Assign a Value to a Variable?
How Do I Specify a Conditional Instruction?
Dialogs
Macro Type
New Macro
References
Function Browser
Object Browser
Grid Settings
UserDialog Editor
Macro Properties
Add Macro References?
Importing STB/SCL
Events Warning
Analysis Warnings
STB Import Warning
SCL Import Warning
Menus
Debug - Add Watch
Debug - Run To Cursor
Debug - Step Out
Debug - Step Over
Debug - Step Into
Edit - Outdent
Tools - References
File - Save as Global Macro
Tools - Dialog Editor
View - Object Browser
File - Add to Report - New Report
Tools - Import STB/SCL
Edit - Indent
Tools - Types Browser
Edit - Macro Properties
View - Font
View - Function Browser
Edit - Delete
Tools - Function Browser
File - Add to Workbook - Add to Analysis Workbook
File - Add to Workbook - New Workbook
File - Add to Report - Add to Analysis Report
Run - Run Macro
Run - Break
Run - Reset
Debug - Show Next Statement
Debug - Clear All Breakpoints
Debug - Set Next Statement
Debug - Quick Watch
Debug - Toggle Breakpoint
Tools - Object Browser
StaDev
StaGetVarName
StaGetValueForLabel
StaGetCaseData
StaGetVarFormat
StaGetNVars
StaGetVarLabelByIndex
StaGetFileHeader
StaSetData
StaGetAllMD
StaSetCaseNameLength
StaGetNumVarLabels
StaSetCaseName
StaSetVarName
StaOpenFile
StaSetFileHeader
StaSetVarMD
StaSetCaseData
StaSetVarLongName
StaSetVarFormat
StaCreateFile
StaDeleteLabelForValue
StaDeleteVars
StaCloseFile
StaAddVars
StaGetNCases
StaAddCases
StaDeleteCases
StaDeleteLabel
Migrating from StaDev to Statistica's COM Architecture
StaGetVarLongName
StaGetVarMD
StaGetLongLabelForValue
StaGetLabelForValue
StaGetData
StaGetCaseNameLength
StaGetCaseName
StaAddLabel
CPlusPlus
Running Basic Statistics in C++
Exporting a Spreadsheet as Text in C++
Statistica Programming in C++ Overview
AddIns
Creating a Statistica Add-In
Installing an Add-In
ProgID
CSharp
Statistica Programming in C# Overview
Creating and Editing a Box & Whisker Plot in C#
VBNET
Sending Custom Statistics to Statistica in VB.NET
Statistica Programming in VB.NET Overview
Delphi
Statistica Programming in Delphi Overview
Running Descriptive Statistics in Delphi
FAQ
General Introduction to Statistica Visual Basic
What are properties, what are methods?
How do I access SVB?
What is Statistica Visual Basic (SVB)?
What are some of the applications of SVB?
What does a simple SVB program look like?
I know nothing about Visual Basic or programming; where do I start?
What is meant by the term "object model?"
Recording Macros Recording an Analysis into a Statistica Visual Basic Program
How do I execute an SVB program from within Statistica?
What exactly is recorded in Master Macros?
How can I record an Analysis Macro?
How can I record a Master Macro (Log of Analyses)?
How can I record my analysis in an SVB program?
What exactly is recorded in Analysis Macros?
General Statistica Visual Basic Language Features Data Types Subroutines Functions Classes Objects
What are local variables vs. global variables?
What is the difference between passing variables to subroutines and functions by value and by reference?
How do I specify a conditional instruction (if ... then)?
How do I execute an expression in a loop?
What are collections?
I already know about Visual Basic; Does SVB support advanced language features?
What are subroutines and functions?
How do I assign a value to a variable?
Which data types are supported in SVB?
Can I expand my SVB by calling external DLLs?
Can I define my own class modules and object modules?
Components of Recorded Macros SVB Programs Case Selection Conditions Output Options
How can I speed up the execution (loading) of Statistica Visual Basic programs by removing unnecessary references (type libraries)?
How are case selection conditions and case weights handled in recorded macros?
How are output options handled in recorded macros?
What are the names for the Statistica reference libraries (modules)?
What are the major "components" in a typical SVB macro program?
Statistica Visual Basic Development Environment
How can I get on-line help?
How are errors handled during the execution of an SVB program?
What is the Function Browser?
How are non-fatal errors (warnings) handled during the execution of an SVB program?
Does SVB include a "Dialog Painter"?
Does the SVB environment provide tools for debugging?
Using SVB To Manage Statistica Analyses DataFiles Results Spreadsheets Graphs
How can I change the font (or other aspects) of numbers in spreadsheets?
Which kind of output can I create with SVB?
How can I access results spreadsheets, graphs, workbooks, etc.?
How can I create a blank graph, and set data directly into the graph?
How can I attach a macro program to a toolbar button (keyboard command, or menu option)?
What is the RouteOutput method; what is the AnalysisOutput object?
How is SVB integrated with Statistica data (input), analyses and output?
How can I modify the appearance of graphs (e.g., axis labels) from within SVB?
How can other aspects of graphs be modified?
Can I use SVB to develop "extensions" of Statistica and my own new "modules?"
Running Statistica Visual Basic Programs from Other Applications
How can I run a Statistica analysis from Microsoft Excel?
Can I execute an SVB program from within other Visual Basic compatible applications?
Handling Large Computational Problems Matrix Operations
What is the capacity of SVB, and can it handle large computational problems?
How can I use the data in a spreadsheet with matrix functions? (How do I copy data efficiently from spreadsheets to matrices, and back?)
Does SVB support matrix operations?
Creating and Managing Custom Dialogs in Statistica Visual Basic
Can I create custom dialogs and other interactive user input controls in SVB?
How are the different controls in custom dialogs "serviced?"
Customizing the Appearance and Behavior of Statistica via Statistica Visual Basic
Can I customize the toolbars and menus via SVB?
How can I create a cell-function spreadsheet using spreadsheet events?
What are application events and how can they be controlled from SVB?
Advanced
SVBProgramingEnvironment
Defining Class Modules and Object Modules
Calling Functions in External DLLs
Limitations and Differences between SVB and Visual Basic in Other Applications
The Variant Data Type
Collections vs. Arrays
Statistica Visual Basic (SVB) Programming Environment
A Simple Message Box, and If..Then..End If Block
Global Variables, Passing Arguments By Value (ByVal) or By Reference (ByRef)
Objects, Methods, and Properties
Performing Computations, Data Types, Subroutines, Functions
SVBObjectModel
The Statistica VB Object Model
A Simple Statistica Visual BASIC (SVB) Program
Macro (SVB) Programs Example - Retrieving a Collection of Spreadsheets
Macro (SVB) Programs Example - Accessing Results Spreadsheet Data
RecordingSimpleMacroPrograms
Example - Recording a Simple Macro with Modal Dialog Specifications
Example - Recording an Analysis Macro
Example - Recording a Simple Macro
Recording Simple Macro (SVB) Programs, Documenting an Analysis
Common Elements of Recorded Macro (SVB) Programs
Recording Macros - Automatic Programming
Recording Case Selection Conditions and Case Weights
Recording Analysis Output
Recording Data Editing Operations
Recording Data File Selections
Master Macros
EditingCustomizingSVB
Debugging a Macro Program
General Help Features of the Statistica VB Environment
Error Handling
Macro (SVB) Programs Example - Customizing a Simple Descriptive Statistics Macro
Macro (SVB) Programs Example - Statistica Dialog Box for Selecting Data Files
Macro (SVB) Programs Example - Statistica Dialog Boxes for Selecting Variables
Using Arrays of Values to Set the .Variables Property
Editing and Customizing Recorded Macro (SVB) Programs
Non-Fatal Errors (Warnings) that Occur
StatisticaLibrariesandModules
Accessing Statistica Visual Basic Libraries
Macro (SVB) Programs Example - Accessing Multiple Modules in a Single Program
Statistica Libraries and Modules
BasicFunctionsforEditingObjects
Macro (SVB) Programs Example - Creating a Custom Spreadsheet
Macro (SVB) Programs Example - Copying Cells from a Spreadsheet into a Report
Macro (SVB) Programs Example - Customizing Workbooks
Macro (SVB) Programs Example - Customizing Graphs
Macro (SVB) Programs Example - Customizing Reports
Macro (SVB) Programs Example - Formatting Cells in Spreadsheets
Basic Functions for Editing Objects (Spreadsheets, Graphs, etc.)
CustomDialogs
Custom Dialog Boxes; Custom User Interfaces
A Simple Dialog Box in Statistica Visual Basic
Servicing Option Buttons, Options Buttons, List Boxes, etc.
Macro (SVB) Programs Example - Writing a Custom Statistica Application with User-Defined Dialog Boxes
Servicing Complex Dialog Boxes via Dialog Functions (DialogFunc)
OpeningNewApplications
Macro (SVB) Programs Example - Printing Statistica Documents
Macro (SVB) Programs Example - Opening and Saving Statistica Documents
Macro (SVB) Programs Example - Launching a New Statistica Application
Opening New Applications, Opening Files, Printing
CustomizingToolbarsMenus
Macro (SVB) Programs Example - Customizing Toolbars and Menus via Statistica Visual Basic
Macro (SVB) Programs Example - Adding a Custom Toolbar via a SVB Macro Program
CallingStatisticainOtherApplications
Calling Statistica from Visual Basic in Other Applications (Excel, Word, etc.)
Macro (SVB) Programs Example - Accessing Statistica Visual Basic Functions from Microsoft Excel
Macro (SVB) Programs Example - Accessing Statistica Visual Basic Functions from Microsoft Word
Events
Report Document-Level Events
Macro Event Behavior
Spreadsheet Document-Level Events
Document-Level Events Example
Document Events
Workbook Document-Level Events
Graph Document-Level Events
Controlling Statistica Events with SVB Programs
Application-Level Events Example
Analysis-Level Events Example
Application-Level Events
Batch (ByGroup) Analysis
Descriptive Statistics
Correlation Matrices
t-Test, Independent, by Groups
t-Test for Independent Samples, by Variables
t-Test, Dependent Samples
t-Test, Single Sample
Frequency Tables
Breakdown and One-Way ANOVA
Breakdown Non-Factorial Tables
Crosstabulation Tables
Stub and Banner Tables
Standard Multiple Regression
Stepwise Multiple Regression
Main Effects ANOVA
Factorial ANOVA
Repeated Measures ANOVA
Observed vs. Expected Chi-Square
Correlations (Spearman, Kendall Tau, Gamma)
Comparing Two Independent Samples (Groups)
Comparing Multiple Indep. Samples (Groups)
Comparing Two Dependent Samples (Variables)
Comparing Multiple Dep. Samples (Variables)
Distribution Fitting
Advanced Linear and Nonlinear Models
General Linear Models
Main Effects Linear Models
Factorial ANCOVA MANCOVA Models
Repeated Measure Models
Response Surface and Mixture Models
Stepwise and Best Subset Probit Regression
Stepwise and Best Subset Logit Regression
Generalized Linear Models
Best-Subset and Stepwise Regression
Best-Subset and Stepwise ANCOVA
General Best-Subset and Stepwise Regression
PLS Regression Models
General PLS Models
NIPALS Algorithms Deployment
NIPALS Algorithm for PCA
NIPALS Algorithm for PLS
Variance Components
Life Tables and Distributions
Kaplan-Meier Product-Limit Method
Comparing Survival in Two Groups
Comparing Survival in Multiple Groups
Regression Models
Quick Logit Regression
Quick Probit Regression
User Specified Regression, Least Squares
User Specified Regression and Loss Function
Log-Linear Analysis
Time Series Plots
Single-Series Transformations (x=f(x))
Two-Series Transformation (x=f(x,y))
Differencing, Time Series Transformations
Smoothing Transformations
Simple Fourier-Type Transformations
Autocorrelations and Crosscorrelations
Distributed Lags Analysis
Exponential Smoothing
ARIMA Models
Interrupted ARIMA
Single Series Spectral (Fourier) Analysis
Two Series Spectral (Fourier) Analysis
Seasonal Decomposition (Census I)
X11/Y2K Census Method II Monthly
X11/Y2K Census Method II Quarterly
Multivariate Exploratory Techniques
Cluster Analysis
K-Means Clustering
Tree Clustering (Joining)
Two-Way Joining Clustering
Factor Analysis
Principal Components and Classification Analysis
Canonical Analysis
Reliability and Item Analysis
Classification from Ordered Predictors
Classification from Categorical and Ordered Predictors
Exhaustive (C and RT) Search for Univariate Splits
Correspondence Analysis (CA)
Multiple Correspondence Analysis (MCA)
Multidimensional Scaling
Standard Discriminant Analysis
Stepwise Discriminant Analysis
Best-Subset and Stepwise GDA ANCOVA
General Best-Subset and Stepwise Discriminant Analysis
Industrial Statistics and Six Sigma
Quality Control Charts
X and MR Chart for Variables, SixGraph Summary
X-Bar and R or S Chart for Variables, SixGraph summary
MA X-Bar and R or S Chart for Variables
EWMA X-Bar and R or S Chart for Variables
Attribute Charts (C, U, Np, p)
CuSum Chart for Observations
Pareto Chart
Multiple Stream Process (MSP) X-Bar and R or S Group Control Chart (GCC)
Multiple Stream Process (MSP) X and MR Chart for Variables
Multiple Stream Process (MSP) Attribute Charts (C, U, Np, p)
Hotelling T-Square Control Chart (HTS)
Process Capability Analysis
Generate Gage R and R Design
Analyze Gage R and R Experiments
Sampling Plans
Weibull and Reliability/Failure Time Analysis
Weibull Probability Paper
Cause-Effect (Ishikawa, Fishbone) Diagrams
Experimental Design (DOE)
D- and A- (T-) Optimal Algorithmic Design Creation
Analysis of 2**(K-p) and Screening Designs
Analysis of Mixed 2 and 3 Level Designs
Analysis of 3**(K-p) and Box-Behnken Designs
Analysis of Central Composite Designs
Analysis of Latin Square Designs
Analysis of Taguchi Robust Designs
Analysis of Mixture Designs
Variance Estimation and Precision
Graphs
2D Graphs
2D Histograms
2D Scatterplots
2D Means with Error Plots
2D Box Plots
2D Variability Plots
2D Range Plots
2D Scatter Icon Plots
2D Scatterplots with Histograms
2D Scatterplots with Box Plots
2D Normal Probability Plots
2D Quantile-Quantile Plots
2D Probability-Probability Plots
2D Bar Column Plots
2D Line Plots (Variables)
2D Line Plots (Case Profiles)
2D Sequential Stacked Plots
2D Pie Charts
2D Missing and Range Data Plots
3D Sequential Graphs
Raw Data Plot
Bivariate Histograms
3D Range Plots
3D Box Plot
3D XYZ Graphs
Scatterplots
3D Surface Plots
3D Contour Plots
3D Ternary Plots
3D Categorized XYZ Plots
Categorized 3D Ternary Plots
Categorized Means with Error Bars Plots
Categorized Box Plots
Categorized Normal Probability Plots
Categorized Quantile-Quantile Plots
Categorized Probability-Probability Plots
Categorized 2D Line Plots (Variables)
Categorized Pie Charts
Categorized 3D Ternary Plots
3D Categorized XYZ Plots
Categorized Missing/Range Data Plots
Matrix Plots
2D Icon Plots
Categorized Graphs
Categorized Histograms
Categorized 2D Scatterplots
Categorized Means with Error Bars Plots
Categorized Box Plots
Categorized Normal Probability Plots
Categorized Quantile-Quantile Plots
Categorized Probability-Probability Plots
Categorized 2D Line Plots (Variables)
Categorized Pie Charts
Categorized 3D Ternary Plots
3D Categorized XYZ Plots
Categorized Missing/Range Data Plots
Workspace Nodes
Alarm Trigger Workspace Node Example
Node Customizations
Publish Protected Workspaces
Workspace Node User and Designer Views
Alternating Least Squares Method
Alternating Least Squares Deployment
Big Data Analytics Workspace Nodes
App Market
Azure Machine Learning Workspace Node
Workspace Node: Azure Machine Learning: Quick Tab and Azure Settings Tab
Big Data Analytics Model Execution Workspace Node
Workspace Node: Big Data Analytics Model Execution - Specifications - Select Model Tab
Workspace Node: Big Data Analytics Model Execution - Specifications - Run Model Tab
Workspace Node: Big Data Analytics Model Execution - Results - Quick Tab
Import Delimited Text Files
Workspace Node: Home Tab
Hadoop Nodes
Spark Scala Nodes
HDFS Export-Import Text Workspace Node
Workspace Node: HDFS Export Text - Specifications Tab
Workspace Node: Home Tab
Import Delimited Text Files
HDFS Import Text Node Example
In-Database Analytics Workspace Nodes
In Database Analytics: Overview
In Database Correlation Matrices  Specifications: Quick Tab
In Database Correlation Matrices: Specifications Options Tab
In Database Enterprise Data Configuration: Specifications Tab
Workspace Node: Home Tab
Workspace Node: Descriptive Statistics - Results - Advanced Tab / In-Database Descriptive Statistics - Specifications - Advanced Tab
Import Delimited Text Files
In Database Filter Duplicate Cases: Specifications Tab
In Database Logistic Regression:  Specifications  Quick Tab
In Database Logistic Regression: Specifications Advanced Tab
In Database Multiple Regression:  Specifications Quick Tab
In Database Multiple Regression: Specifications Advanced Tab
WS Node - Random Sample Filtering - Specifications Tab / In-Database Random Sample Filtering - Specifications Tab
In Database Sort: Specifications Tab
In Database Sort:  Downstream Tab
Write to Database: Specifications Tab
In Database Write to Database: Downstream Tab
Data Mining Workspace Nodes
Clustering/Grouping Workspace Nodes
Workspace Node: Generalized Cluster Analysis - Specifications - Quick Tab
Workspace Node: Generalized Cluster Analysis - Specifications - Options Tab
Workspace Node: Generalized Cluster Analysis - Specifications - Validation Tab
Workspace Node: Generalized Cluster Analysis - Results - Quick Tab
Workspace Node: Generalized Cluster Analysis - Results - Advanced Tab
Workspace Node: Code Generator Tab
Workspace Node: Downstream Tab
Workspace Node: Home Tab
Deployment Workspace Nodes
Goodness of Fit Workspace Node
Workspace Node: Goodness of Fit - Specifications - Quick Tab
Workspace Node: Goodness of Fit - Specifications - Advanced Tab
Workspace Node: Goodness of Fit - Results - Quick Tab
Workspace Node: Goodness of Fit - Results - Advanced Tab
Workspace Node: Downstream Tab
Workspace Node: Home Tab
PMML Model Workspace Node
Workspace Node: Deployment Using PMML - Details Tab
Workspace Node: Deployment Using PMML - PMML Tab
Workspace Node: Home Tab
Model Comparison Node
Rapid Deployment Workspace Node
WS Node - Rapid Deployment - Specifications - Quick Tab
WS Node - Rapid Deployment - Specifications - Lift Chart Tab
WS Node - Rapid Deployment - Specifications - Profit Chart/ROC Curve Tab
WS Node - Rapid Deployment - Specifications - Confusion Matrix Tab
Workspace Node: Rules - Specifications Tab
Write Spreadsheet to Database - Configuration
Write Spreadsheet to Database - Advanced Settings
Learning Workspace Nodes
Machine Learning Workspace Nodes
Workspace Node: K-Nearest Neighbors - Specifications - Quick Tab
Workspace Node: K-Nearest Neighbors - Specifications - Sampling Tab
Workspace Node: K-Nearest Neighbors - Specifications - Options Tab
Workspace Node: K-Nearest Neighbors - Specifications - Cross-Validation Tab
Workspace Node: K-Nearest Neighbors - Specifications - Memory Usage Tab
Workspace Node: K-Nearest Neighbors - Results - Quick Tab
Workspace Node: K-Nearest Neighbors - Results - Plots Tab
Workspace Node: Code Generator Tab
Workspace Node: Naive Bayes Classifiers - Specifications - Quick Tab
Workspace Node: Naive Bayes Classifiers - Specifications - Sampling Tab
Workspace Node: Naive Bayes Classifiers - Specifications - Distribution Tab
Workspace Node: Naive Bayes Classifiers - Specifications - Memory Usage Tab
Workspace Node: Naive Bayes Classifiers - Results - Quick Tab
Workspace Node: Naive Bayes Classifiers - Results - Plots Tab
Workspace Node: Naive Bayes Classifiers - Results - Prior Tab
Workspace Node: Naive Bayes Classifiers - Results - Posterior Tab
Workspace Node: Support Vector Machines - Specifications - Quick Tab
Workspace Node: Support Vector Machines - Specifications - Sampling Tab
Workspace Node: Support Vector Machines - Specifications - SVM Tab
Workspace Node: Support Vector Machines - Specifications - Kernels Tab
Workspace Node: Support Vector Machines - Specifications - Cross-Validation Tab
Workspace Node: Support Vector Machines - Specifications - Training Tab
Workspace Node: Support Vector Machines - Results - Quick Tab
Workspace Node: Support Vector Machines - Results - Plots Tab
Neural Networks Workspace Nodes
SANN Classification Workspace Node
Workspace Node: SANN Classification - Specifications - Quick Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Sampling (CNN and ANS) Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - Subsampling Tab
Workspace Node: SANN Classification/Time Series Classification - Specifications - Quick Specification Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - MLP Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - MLP Activation Functions Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - Weight Decay Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Initialization Tab
Workspace Node: SANN Regression/Classification/Time Series - Results - Samples Tab
Workspace Node: SANN Classification/Time Series Classification - Results - Predictions Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Results - Graphs Tab
Workspace Node: SANN Classification/Time Series Classification - Results - Details Tab
Workspace Node: SANN Classification/Time Series Classification - Results - Liftcharts Tab
SANN Regression Workspace Node
Workspace Node: SANN Regression - Specifications - Quick Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Sampling (CNN and ANS) Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - Subsampling Tab
Workspace Node: SANN Regression/Time Series Regression - Specifications - Quick Specification Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - MLP Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - MLP Activation Functions Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - Weight Decay Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Initialization Tab
Workspace Node: SANN Regression/Classification/Time Series - Results - Samples Tab
Workspace Node: SANN Regression/Time Series Regression - Results - Predictions Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Results - Graphs Tab
Workspace Node: SANN Regression/Time Series Regression - Results - Details Tab
SANN Clustering Workspace Node
Workspace Node: SANN Clustering - Specifications - Quick Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Sampling (CNN and ANS) Tab
Workspace Node: SANN Clustering - Specifications - Quick (Kohonen) Tab
Workspace Node: SANN Clustering - Specifications - Kohonen Training Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Initialization Tab
Workspace Node: SANN Clustering - Results - Samples Tab
Workspace Node: SANN Clustering - Results - Predictions (Kohonen) Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Results - Graphs Tab
Workspace Node: SANN Clustering - Results - Kohonen Network Tab
SANN Time Series (Classification) Workspace Node
Workspace Node: SANN Time Series (Classification) - Specifications - Quick Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Sampling (CNN and ANS) Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - Subsampling Tab
Workspace Node: SANN Time Series (Regression/Classification) - Specifications - Time Series Tab
Workspace Node: SANN Classification/Time Series Classification - Specifications - Quick Specification Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - MLP Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - MLP Activation Functions Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - Weight Decay Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Initialization Tab
Workspace Node: SANN Regression/Classification/Time Series - Results - Samples Tab
Workspace Node: SANN Classification/Time Series Classification - Results - Predictions Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Results - Graphs Tab
Workspace Node: SANN Classification/Time Series Classification - Results - Details Tab
Workspace Node: SANN Classification/Time Series Classification - Results - Liftcharts Tab
Workspace Node: SANN Time Series (Regression/Classification) - Results - Time Series Tab
SANN Time Series (Regression) Workspace Node
Workspace Node: SANN Time Series (Regression) - Specifications - Quick Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Sampling (CNN and ANS) Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - Subsampling Tab
Workspace Node: SANN Time Series (Regression/Classification) - Specifications - Time Series Tab
Workspace Node: SANN Regression/Time Series Regression - Specifications - Quick Specification Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - MLP Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - MLP Activation Functions Tab
Workspace Node: SANN Regression/Classification/Time Series - Specifications - Weight Decay Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Specifications - Initialization Tab
Workspace Node: SANN Regression/Classification/Time Series - Results - Samples Tab
Workspace Node: SANN Regression/Time Series Regression - Results - Predictions Tab
Workspace Node: SANN Regression/Classification/Time Series/Clustering - Results - Graphs Tab
Workspace Node: SANN Regression/Time Series Regression - Results - Details Tab
Workspace Node: SANN Time Series (Regression/Classification) - Results - Time Series Tab
Text Mining Workspace Node
Workspace Node: Word Cloud Generator
Workspace Node: Text Mining - Specifications - Quick Tab
Workspace Node: Text Mining - Specifications - Advanced Tab
Workspace Node: Text Mining - Specifications - Words Tab
Workspace Node: Text Mining - Specifications - Projects Tab
Workspace Node: Text Mining - Specifications - Filters Tab
Workspace Node: Text Mining - Specifications - Characters Tab
Workspace Node: Text Mining - Specifications - Delimiters Tab
Workspace Node: Text Mining - Results - Frequency Measure Tab
Workspace Node: Text Mining - Results - Summary Tab
Workspace Node: Text Mining - Results - Concept Extraction Tab
Workspace Node: Text Mining - Results - Search Tab
Tools Workspace Nodes
Workspace Node: Predictor Screening - Specifications - Quick Tab
Workspace Node: Predictor Screening - Specifications - Advanced Tab
Trees/Partitioning Workspace Nodes
Boosted Trees Workspace Nodes
Boosted Classification Trees Workspace Node
Workspace Node: Boosted Classification Trees - Specifications - Quick Tab
Workspace Node: Boosted Classification Trees - Specifications - Classification Tab
Workspace Node: Boosted Classification Trees - Specifications - Advanced Tab
Workspace Node: Boosted Classification Trees - Results - Quick Tab
Workspace Node: Boosted Classification Trees - Results - Classification Tab
Workspace Node: Boosted Classification Trees - Results - Prediction Tab
Boosted Regression Trees Workspace Node
Workspace Node: Boosted Regression Trees - Specifications - Quick Tab
Workspace Node: Boosted Regression Trees - Specifications - Advanced Tab
Workspace Node: Boosted Regression Trees - Results - Quick Tab
Workspace Node: Boosted Regression Trees - Results - Prediction Tab
I-Trees Workspace Nodes
Advanced Classification CHAID Workspace Node
Workspace Node: Advanced Classification CHAID - Specifications - Quick Tab
Workspace Node: Advanced Classification CHAID - Specifications - Classification Tab
Workspace Node: Advanced Classification CHAID - Specifications - Stopping Tab
Workspace Node: Advanced Classification CHAID - Specifications - Validation Tab
Workspace Node: Advanced Classification CHAID - Specifications - Advanced Tab
Workspace Node: Advanced Classification CHAID - Results - Quick Tab
Workspace Node: Advanced Classification CHAID - Results - Classification Tab
Workspace Node: Advanced Classification CHAID - Results - Prediction Tab
Advanced Regression CHAID Workspace Node
Workspace Node: Advanced Regression CHAID - Specifications - Quick Tab
Workspace Node: Advanced Regression CHAID - Specifications - Stopping Tab
Workspace Node: Advanced Regression CHAID - Specifications - Validation Tab
Workspace Node: Advanced Regression CHAID - Specifications - Advanced Tab
Workspace Node: Advanced Regression CHAID - Results - Quick Tab
Workspace Node: Advanced Regression CHAID - Results - Prediction Tab
Advanced Classification Trees (CRT) Workspace Node
Workspace Node: C&RT Classification - Specifications - Quick Tab
Workspace Node: C&RT Classification - Specifications - Classification Tab
Workspace Node: C&RT Classification - Specifications - Stopping Tab
Workspace Node: C&RT Classification - Specifications - Validation Tab
Workspace Node: C&RT Classification - Specifications - Advanced Tab
Workspace Node: C&RT Classification - Results - Quick Tab
Workspace Node: C&RT Classification - Results - Classification Tab
Workspace Node: C&RT Classification - Results - Prediction Tab
Advanced Regression Trees (CRT) Workspace Node
Workspace Node: C&RT Regression - Specifications - Quick Tab
Workspace Node: C&RT Regression - Specifications - Stopping Tab
Workspace Node: C&RT Regression - Specifications - Validation Tab
Workspace Node: C&RT Regression - Specifications - Advanced Tab
Workspace Node: C&RT Regression - Results - Quick Tab
Workspace Node: C&RT Regression - Results - Prediction Tab
MARSplines Workspace Node
Workspace Node: MARSplines - Specifications - Quick Tab
Workspace Node: MARSplines - Specifications - Options Tab
Workspace Node: MARSplines - Results - Quick Tab
Workspace Node: MARSplines - Results - Graphs Tab
Random Forest Workspace Nodes
Random Forest Classification Workspace Node
Workspace Node: Random Forest Classification - Specifications - Quick Tab
Workspace Node: Random Forest Classification - Specifications - Classification Tab
Workspace Node: Random Forest Classification - Specifications - Advanced Tab
Workspace Node: Random Forest Classification - Specifications - Stopping Condition Tab
Workspace Node: Random Forest Classification - Results - Quick Tab
Workspace Node: Random Forest Classification - Results - Classification Tab
Workspace Node: Random Forest Classification - Results - Prediction Tab
Random Forest Regression Workspace Node
Workspace Node: Random Forest Regression - Specifications - Quick Tab
Workspace Node: Random Forest Regression - Specifications - Advanced Tab
Workspace Node: Random Forest Regression - Specifications - Stopping Condition Tab
Workspace Node: Random Forest Regression - Results - Quick Tab
Workspace Node: Random Forest Regression - Results - Prediction Tab
Data Tab Workspace Nodes
Data Health Check Summary Workspace Node
Workspace Node: Data Health Check Summary - Specifications - Quick Tab
Workspace Node: Data Health Check Summary - Specifications - Sparse Data Tab
Workspace Node: Data Health Check Summary - Specifications - Outliers Tab
Workspace Node: Data Health Check Summary - Specifications - Invariant Variables Tab
Workspace Node: Data Health Check Summary - Specifications - Redundancy Tab
Workspace Node: Data Health Check Summary - Specifications - Options Tab
Workspace Node: Data Health Check Summary - Results Tab
Workspace Node: Home Tab
Filter/Recode Workspace Nodes
Workspace Node: Filter Duplicate Cases - Specifications Tab
Workspace Node: Home Tab
Workspace Node: Filter Sparse Data - Specifications Tab
Workspace Node: Missing Data Imputation - Specifications Tab
Workspace Node: Process Invariant Variables - Specifications Tab
Workspace Node: Process Missing Data - Specifications Tab
Workspace Node: Recode Outliers - Specifications Tab
Get External Data Workspace Nodes
Workspace Node: Enterprise Data Configuration - Specifications Tab
Workspace Node: Home Tab
Workspace Node: Import Excel - Specifications Tab
Merge Workspace Node
Workspace Node: Cartesian Variables - Specifications Tab
Workspace Node: Home Tab
Workspace Node: Concatenate Variables - Specifications Tab
Workspace Node: Match Cases - Specifications Tab
Workspace Node: Merge Cases - Specifications Tab
Workspace Node: Merge Text Labels - Specifications Tab
Workspace Node: Merge Variables - Specifications Tab
Workspace Node: Query Spreadsheets - Specifications Tab
Workspace Node: Rank - Specifications Tab
Workspace Node: Recode Variable - Specifications Tab
Workspace Node: Rules - Specifications Tab
Sampling Workspace Node
WS Node - Define Training Testing Sample - Specifications Tab
WS Node - Random Sample Filtering - Specifications Tab / In-Database Random Sample Filtering - Specifications Tab
Select Dependent Variables and Predictors - Quick Tab
Select Dependent Variables and Predictors - Advanced Tab
Workspace Node: Sort - Specifications Tab
Workspace Node: Stacking - Specifications Tab
Workspace Node: Subset - Specifications Tab
Workspace Node: Transformations of Variables - Specifications Tab
Workspace Node: Unstacking - Specifications Tab
Deep Learning Overview
Deep Learning Dialog box
Graphs Workspace Nodes
2D Workspace Nodes
2D Bag Plots Workspace Node
Workspace Node: 2D Bag Plots - Quick Tab
Workspace Node: 2D Bar Column Plots - Appearance Tab
Workspace Node: Graphs - Categorized Tab
Workspace Node: Graphs - Options 1 Tab
Workspace Node: Graphs - Options 2 Tab
Workspace Node: Home Tab
2D Bar/Column Plots Workspace Node
Workspace Node: 2D Bar Column Plots - Quick Tab
Workspace Node: 2D Bar Column Plots - Appearance Tab
2D Box Plots Workspace Node
Workspace Node: 2D Box Plots - Quick Tab
Workspace Node: 2D Box Plots - Advanced Tab
Workspace Node: 2D Box Plots - Appearance Tab
Workspace Node: Graphs - Categorized Tab
2D Histograms Workspace Node
Workspace Node: 2D Histograms - Quick Tab
Workspace Node: 2D Histograms - Advanced Tab
Workspace Node: 2D Histograms - Appearance Tab
Workspace Node: Graphs - Categorized Tab
2D Line Plots (Case Profiles) Workspace Node
Workspace Node: 2D Line Plots (Case Profiles) - Quick Tab
Workspace Node: 2D Line Plots (Case Profiles) - Appearance Tab
2D Line Plots (Variables) Workspace Node
Workspace Node: 2D Line Plots (Variables) - Quick Tab
Workspace Node: 2D Line Plots (Variables) - Appearance Tab
Workspace Node: Graphs - Categorized Tab
2D Means with Error Plots Workspace Node
Workspace Node: 2D Means with Error Plots - Quick Tab
Workspace Node: 2D Means with Error Plots - Advanced Tab
Workspace Node: 2D Means with Error Plots - Appearance Tab
Workspace Node: Graphs - Categorized Tab
2D Missing and Out-of-Range Data Plots Workspace Node
WS Node: 2D Missing and Out-of-Range Data Plots - Quick Tab
WS Node: 2D Missing and Out-of-Range Data Plots - Advanced Tab
WS Node: 2D Missing and Out-of-Range Data Plots - Appearance Tab
2D Normal Probability Plots Workspace Tab
Workspace Node: 2D Normal Probability Plots - Quick Tab
Workspace Node: 2D Normal Probability Plots - Appearance Tab
2D Pie Charts Workspace Node
Workspace Node: 2D Pie Charts - Quick Tab
Workspace Node: 2D Pie Charts - Advanced Tab
Workspace Node: 2D Pie Charts - Appearance Tab
2D Probability-Probability Plots Workspace Node
Workspace Node: 2D Probability-Probability Plots - Quick Tab
Workspace Node: 2D Probability-Probability Plots - Advanced Tab
Workspace Node: 2D Probability-Probability Plots - Appearance Tab
2D Quantile-Quantile Plots Workspace Node
Workspace Node: 2D Quantile-Quantile Plots - Quick Tab
Workspace Node: 2D Quantile-Quantile Plots - Advanced Tab
Workspace Node: 2D Quantile-Quantile Plots - Appearance Tab
2D Range Plots Workspace Node
Workspace Node: 2D Range Plots - Quick Tab
Workspace Node: 2D Range Plots - Advanced Tab
Workspace Node: 2D Range Plots - Appearance Tab
2D Scatter Icon Plots Workspace Node
Workspace Node: 2D Scatter Icon Plots - Quick Tab
Workspace Node: 2D Scatter Icon Plots - Appearance Tab
2D Scatter Image Plots Workspace Node
Workspace Node: 2D Scatter Image Plots - Quick Tab
Workspace Node: 2D Scatter Image Plots - Appearance Tab
2D Scatterplots Workspace Node
Workspace Node: 2D Scatterplots - Quick Tab
Workspace Node: 2D Scatterplots - Advanced Tab
Workspace Node: 2D Scatterplots - Appearance Tab
2D Scatterplots with Box Plots Workspace Node
Workspace Node: 2D Scatterplots with Box Plots - Quick Tab
Workspace Node: 2D Scatterplots with Box Plots - Advanced Tab
Workspace Node: 2D Scatterplots with Box Plots - Appearance Tab
2D Scatterplots with Error Bars Workspace Node
Workspace Node: 2D Scatterplots with Error Bars - Quick Tab
Workspace Node: 2D Scatterplots with Error Bars - Appearance Tab
2D Scatterplots with Histograms Workspace Node
Workspace Node: 2D Scatterplots with Histograms - Quick Tab
Workspace Node: 2D Scatterplots with Histograms - Advanced Tab
Workspace Node: 2D Scatterplots with Histograms - Appearance Tab
2D Sequential/Stacked Plots Workspace Node
Workspace Node: 2D Sequential/Stacked Plots - Quick Tab
Workspace Node: 2D Sequential/Stacked Plots - Appearance Tab
3D Sequential Graphs Workspace Nodes
3D Bivariate Histograms Workspace Node
Workspace Node: 3D Bivariate Histograms - Quick Tab
Workspace Node: 3D Bivariate Histograms - Advanced Tab
Workspace Node: 3D Bivariate Histograms - Appearance Tab
3D Box Plots Workspace Node
Workspace Node: 3D Box Plots - Quick Tab
Workspace Node: 3D Box Plots - Advanced Tab
Workspace Node: 3D Box Plots - Appearance Tab
3D Range Plots Workspace Node
Workspace Node: 3D Range Plots - Quick Tab
Workspace Node: 3D Range Plots - Appearance Tab
3D Raw Data Plots Workspace Node
Workspace Node: 3D Raw Data Plots - Quick Tab
Workspace Node: 3D Raw Data Plots - Appearance Tab
3D XYZ Graphs Workspace Nodes
3D Contour Plots Workspace Node
Workspace Node: Graphs - Categorized Tab
Workspace Node: Graphs - Options 1 Tab
Workspace Node: Graphs - Options 2 Tab
Workspace Node: Home Tab
3D Scatter Image Plots Workspace Node
Workspace Node: 3D Scatter Image Plots - Quick Tab
Workspace Node: 3D Scatter Image Plots - Appearance Tab
3D Scatterplots Workspace Node
Workspace Node: 3D Scatterplots - Quick Tab
Workspace Node: 3D Scatterplots - Advanced Tab
Workspace Node: 3D Scatterplots - Appearance Tab
3D Surface Plots Workspace Node
Workspace Node: 3D Surface Plots - Quick Tab
Categorized 3D Ternary Plots Workspace Node
Workspace Node: Categorized 3D Ternary Plots - Quick Tab
Workspace Node: Categorized 3D Ternary Plots - Advanced Tab
Workspace Node: Categorized 3D Ternary Plots - Appearance Tab
Categorized 3D XYZ Plots Workspace Node
Workspace Node: Categorized 3D XYZ Plots - Quick Tab
Workspace Node: Categorized 3D XYZ Plots - Advanced Tab
Workspace Node: Categorized 3D XYZ Plots - Appearance Tab
Icon Plots Workspace Node
Workspace Node: Icon Plots - Quick Tab
Workspace Node: Icon Plots - Appearance Tab
Matrix Plots Workspace Node
Workspace Node: Matrix Plots - Quick Tab
Workspace Node: Matrix Plots - Advanced Tab
Workspace Node: Matrix Plots - Appearance Tab
Stability Analysis Workspace Node
WS Node: Stability Analysis - General Tab
WS Node: Stability Analysis - Advanced Tab
WS Node: Stability Analysis - Specification Limits Tab
WS Node: Stability Analysis - Maximum No. of Time Points Tab
WS Node: Stability Analysis - Number of Decimal Places Tabs
Statistics Workspace Nodes
Advanced/Multivariate Workspace Nodes
Advanced Models Workspace Nodes
Cox Proportional Hazards Workspace Node
Workspace Node: Cox Proportional Hazards - Specifications - Quick Tab
Workspace Node: Cox Proportional Hazards - Specifications - Options Tab
Workspace Node: Cox Proportional Hazards - Specifications - Advanced Tab
Workspace Node: Cox Proportional Hazards - Results - Quick Tab
Workspace Node: Cox Proportional Hazards - Results - Survivor Function Tab
Workspace Node: Cox Proportional Hazards - Results - Plots Tab
Workspace Node: Code Generator Tab
Workspace Node: Downstream Tab
Workspace Node: Home Tab
GLM Custom Design Workspace Node
Workspace Node: GLM Custom Design - Specifications - Quick Tab
Workspace Node: GLM Custom Design - Specifications - Options Tab
Workspace Node: GLM Custom Design - Results - Summary Tab
Workspace Node: GLM Custom Design - Results - Means Tab
Workspace Node: GLM Custom Design - Results - Planned Comps Tab
Workspace Node: GLM Custom Design - Results - Post-hoc Tab
Workspace Node: GLM Custom Design - Results - Assumptions Tab
Workspace Node: GLM Custom Design - Results - Profiler Tab
Workspace Node: GLM Custom Design - Results - Custom Tests Tab
Workspace Node: GLM Custom Design - Results - Residuals 1 Tab
Workspace Node: GLM Custom Design - Results - Residuals 2 Tab
Workspace Node: GLM Custom Design - Results - Matrix Tab
Workspace Node: GLM Custom Design - Results - Report Tab
GLZ Custom Design Workspace Node
Workspace Node: GLZ Custom Design - Specifications - Quick Tab
Workspace Node: GLZ Custom Design - Specifications - Model Specification Tab
Workspace Node: GLZ Custom Design - Specifications - Advanced Tab
Workspace Node: GLZ Custom Design - Results - Summary Tab
Workspace Node: GLZ Custom Design - Results - Residuals 1 Tab
Workspace Node: GLZ Custom Design - Results - Residuals 2 Tab
Workspace Node: GLZ Custom Design - Results - Means Tab
Workspace Node: Code Generator Tab
Nonlinear Estimation Workspace Node
Workspace Node: Nonlinear Estimation - Specifications - Regression Specification Tab
Workspace Node: Nonlinear Estimation - Specifications - Quick Tab
Workspace Node: Nonlinear Estimation - Specifications - Advanced Tab
Workspace Node: Nonlinear Estimation - Results - Quick Tab
Workspace Node: Nonlinear Estimation - Results - Advanced Tab
Workspace Node: Nonlinear Estimation - Results - Residuals Tab
Workspace Node: Nonlinear Estimation - Results - Review Tab
MultExploratory Workspace Nodes
Cluster Workspace Nodes
K-Means Clustering Workspace Node
Workspace Node: K-Means Clustering - Specifications - Quick Tab
Workspace Node: K-Means Clustering - Specifications - Advanced Tab
Workspace Node: K-Means Clustering - Results - Quick Tab
Workspace Node: K-Means Clustering - Results - Advanced Tab
Workspace Node: Downstream Tab
Workspace Node: Home Tab
Tree Clustering (Joining) Workspace Node
Workspace Node: Tree Clustering (Joining) - Specifications - Quick Tab
Workspace Node: Tree Clustering (Joining) - Specifications - Advanced Tab
Workspace Node: Tree Clustering (Joining) - Results - Quick Tab
Workspace Node: Tree Clustering (Joining) - Results - Advanced Tab
Two-Way Joining Clustering Workspace Node
Workspace Node: Two-Way Joining Clustering - Specifications - Quick Tab
Workspace Node: Two-Way Joining Clustering - Results - Quick Tab
Principal Components And Classification Analysis
Workspace Node: Principal Components and Classification Analysis - Specifications - Quick Tab
Workspace Node: Principal Components and Classification Analysis - Results - Quick Tab
Workspace Node: Principal Components and Classification Analysis - Results - Variables Tab
Workspace Node: Principal Components and Classification Analysis - Results - Cases Tab
Workspace Node: Principal Components and Classification Analysis - Results - Descriptives Tab
Basic Statistics Workspace Nodes
Breakdown and One-Way ANOVA Individual Tables Workspace Node
Workspace Node: Breakdown and One-Way ANOVA Individual Tables - Specifications - Quick Tab
Workspace Node: Breakdown and One-Way ANOVA Individual Tables - Results - Quick Tab
Workspace Node: Breakdown and One-Way ANOVA Individual Tables - Results - Descriptives Tab
Workspace Node: Breakdown and One-Way ANOVA Individual Tables - Results - ANOVA & Tests Tab
Workspace Node: Breakdown and One-Way ANOVA Individual Tables - Results - Correlations Tab
Workspace Node: Breakdown and One-Way ANOVA Individual Tables - Results - Post-hoc Tab
Breakdown Non-Factorial Tables Workspace Node
Workspace Node: Breakdown Non-Factorial Tables - Specifications - Quick Tab
Workspace Node: Breakdown Non-Factorial Tables - Results - Descriptives Tab
Workspace Node: Breakdown Non-Factorial Tables - Results - Robust Tab
Correlation Matrices Workspace Node
In Database Correlation Matrices  Specifications: Quick Tab
Workspace Node: Correlation Matrices - Results - Quick Tab
Workspace Node: Correlation Matrices - Results - Advanced Tab
Workspace Node: Correlation Matrices - Results - Options Tab
Workspace Node: Correlation Matrices - Results - Color Maps Tab
In Database Correlation Matrices: Specifications Options Tab
Crosstabulation Tables Workspace Node
Workspace Node: Crosstabulation Tables - Specifications - Quick Tab
Workspace Node: Crosstabulation Tables - Results - Quick Tab
Workspace Node: Crosstabulation Tables - Results - Advanced Tab
Workspace Node: Crosstabulation Tables - Results - Options Tab
Descriptive Statistics Workspace Node
Workspace Node: Descriptive Statistics - Specifications - Quick Tab
Workspace Node: Descriptive Statistics - Results - Quick Tab
Workspace Node: Descriptive Statistics - Results - Advanced Tab / In-Database Descriptive Statistics - Specifications - Advanced Tab
Workspace Node: Descriptive Statistics - Results - Robust Tab
Workspace Node: Descriptive Statistics - Results - Normality Tab
Workspace Node: Descriptive Statistics - Results - Prob. & Scatterplots Tab
Workspace Node: Descriptive Statistics - Results - Categ. Plots Tab
Workspace Node: Descriptive Statistics - Results - Options Tab
Frequency Tables Workspace Node
Workspace Node: Frequency Tables - Specifications - Quick Tab
Workspace Node: Frequency Tables - Results - Quick Tab
Workspace Node: Frequency Tables - Results - Advanced Tab
Workspace Node: Frequency Tables - Results - Options Tab
Workspace Node: Frequency Tables - Results - Descriptives Tab
Workspace Node: Frequency Tables - Results - Normality Tab
Workspace Node: Frequency Tables - Results - Box-Whisker Type Tab
Stub and Banner Tables Workspace Node
Workspace Node: Stub and Banner Tables - Specifications - Quick Tab
Workspace Node: Stub and Banner Tables - Results - Quick Tab
Workspace Node: Stub and Banner Tables - Results - Advanced Tab
Workspace Node: Stub and Banner Tables - Results - Options Tab
t-Test, Dependent Samples Workspace Node
Workspace Node: t-Test, Dependent Samples - Specifications - Quick Tab
Workspace Node: t-Test, Dependent Samples - Results - Quick Tab
Workspace Node: t-Test, Dependent Samples - Results - Advanced Tab
Workspace Node: t-Test, Dependent Samples - Results - Options Tab
t-Test, Independent, by Groups Workspace Node
Workspace Node: t-Test, Independent, by Groups - Specifications - Quick Tab
Workspace Node: t-Test, Independent, by Groups - Results - Quick Tab
Workspace Node: t-Test, Independent, by Groups - Results - Advanced Tab
Workspace Node: t-Test, Independent, by Groups - Results - Options Tab
t-Test, Independent Samples, by Variables Workspace Node
Workspace Node: t-Test, Independent Samples, by Variables - Specifications - Quick Tab
Workspace Node: t-Test, Independent Samples, by Variables - Results - Quick Tab
Workspace Node: t-Test, Independent Samples, by Variables - Results - Options Tab
t-Test, Single Sample Workspace Node
Workspace Node: t-Test, Single Sample - Specifications - Quick Tab
Workspace Node: t-Test, Single Sample - Results - Quick Tab
Workspace Node: t-Test, Single Sample - Results - Advanced Tab
Workspace Node: t-Test, Single Sample - Results - Options Tab
Industrial Statistics Workspace Nodes
SPC Charts - Alarm Management
SPC Chart - Chart 1
SPC Chart - Chart Options 1
SPC Charts - Chart 2
SPC Charts - Chart Option 2
SPC Charts - Chart Option 3
SPC Charts - Chart Option 4
SPC Charts - Chart Settings
SPC Charts - Custom Titles
SPC Charts - General Tab
SPC Charts - Graph Customization
SPC Charts - Run Rules
SPC Charts - Run Rules Types 1
SPC Charts - Run Rules Types 2
SPC Charts - Specification Limits
SPC Charts - Variance Chart Center Line
SPC Charts - Variance Chart Control Limits
SPC Charts - Variance Chart Warning Limits
SPC Charts - Vertical Delimiter
SPC Charts - X Chart Center/Sigma
SPC Charts - X Chart Control Limits
SPC Charts - X Chart Warning Limits
More Distributions Workspace Nodes
Design Simulation Workspace Node
Workspace Node: Design Simulation - Specifications - Quick Tab
Workspace Node: Home Tab
Fit Distributions Workspace Node
Workspace Node: Fit Distributions - Specifications - Quick Tab
Workspace Node: Fit Distributions - Specifications - Continuous Variables Tab
Workspace Node: Fit Distributions - Specifications - Discrete Variables Tab
Workspace Node: Fit Distributions - Specifications - Options Tab
Workspace Node: Fit Distributions - Results - Quick Tab
Workspace Node: Fit Distributions - Results - Save Fit Tab
Run Simulation Workspace Node
Workspace Node: Run Simulation - Specifications - Quick Tab
Working with Analysis Output
Five Channels for Output from Analyses
Global Output Options - Output Manager
Analysis/Graph Output Manager
Output Headers and Footers
Frequently Asked Questions
Can I add custom headers or footers to printed output?
How can I copy/print results summaries?
How do I access help for a specific dialog box?
How do I change the printer setup?
How do I print previously saved results?
How do I print spreadsheets?
How do I select items in the workbook tree?
How can I suppress the printing of gridlines in spreadsheets?
What are workbooks?
What output management options are available in Statistica?
Printing
Before You Print
Setting Basic Print Options - Spreadsheets, Reports, or Graphs
Setting Basic Print Options - Workbook
Using the Print Preview Window
Printing a Spreadsheet
Printing a Report
Printing a Workbook
Importing and Exporting
Importing Text
Importing File
Import Fixed Text Files
Import Delimited Text Files
Advanced Delimited Importing Options
Text Fixed Import Options
Text Free Import Options
Text Free Separators
Example: Text Import
Importing HTML
Opening HTML File
Importing Excel
Opening File
Select Excel Range for the Analysis
Open Excel Workbook
Select Sheet to Import
Open Excel File
Review/Edit Column Types
Change Import Column Type
Exporting Data
Exporting a Spreadsheet as Text in C++
How do I export a Statistica graph to another application?
How do I export data from Statistica to Excel and other foreign data files?
Opening Document Objects in Statistica
Working with Spreadsheets
UnderstandingSpreadsheets
Spreadsheet (Multimedia Table) Overview
Spreadsheet (Multimedia Table) Window Overview
Spreadsheet Formatting Options
Multiple Analysis Support
Unlimited Size of Spreadsheets
Global Spreadsheet Options
Range of Numeric Values that Can Be Entered or Stored in Cells
Matrix Spreadsheets
Matrix File Format
Types of Matrices
Multiple Correlation Matrices
Variable Data Types
Spreadsheet Display Formats
Custom Number Format Codes
Creating a New Spreadsheet
Adding Objects to a Spreadsheet
Dialog Boxes
Auto Format Cells
Custom Max Column Width
Extrapolate Values
Lock Spreadsheet
Select Variable
Sort Performance Warning
Spreadsheet Case Selection Conditions
Spreadsheet Audit Log Settings
Enter reason for change
Spreadsheet Audit Log Viewer
Using Spreadsheets
Opening a Spreadsheet
Locking a Spreadsheet
Unlocking a Spreadsheet
Logging Spreadsheet Changes
Viewing the Spreadsheet Log
Split Scrolling
Selecting an Entire Spreadsheet
Spreadsheet Formulas - Overview
Spreadsheet Log Table
Box-Cox Transformation Overview and Technical Notes
Box-Cox Transformation
Box-Cox Results
Menus
File Menu
Edit Menu
View Menu
Insert Menu
Format Menu
Statistics Menu
Data Mining Menu
Graphs Menu
Tools Menu
Data Menu
Window Menu
Help menu
Shortcut
Add Cases and/or Variables
Format - AutoFit Selection
AutoFit Header Height
Copy Header
Copy Info Box Text
Cut Header
Cut Info Box Text
Format Header
Format Info Box Text
Paste into Header
Paste into Info Box
Select All
Format - Cases - Cloak
Format - Cases - Uncloak
Format - Variables - Cloak
Format - Variables - Uncloak
What are the drag-and-drop facilities?
Can I expand the data file using drag-and-drop?
How does the extrapolation of blocks (AutoFill) work?
Printing a Spreadsheet
Using the Print Preview Window
Saving a Spreadsheet
Exchanging Information (Data, Graphs) with Other Applications
Exporting Data
Importing Data
Using Links
OLE Links
DDE Links - Overview
DDE Links
Creating a New DDE Link
Editing a DDE Link
Updating DDE Links
Disabling a DDE Link
Deleting a DDE Link
DDE Link Types
DDE Link Statements
New DDE Link
Manage DDE Links
Paste Link - Overview
Links
Updating a Paste Link
Opening a Paste Link Source
Changing a Paste Link Source
Breaking a Paste Link
Spreadsheet Formulas - Overview
Spreadsheet Formulas - Syntax Summary
Spreadsheet Formulas - Syntax Operators
Spreadsheet Formulas - Distributions and Their Functions
Spreadsheet Formulas - Math and Statistical Functions
Spreadsheet Formulas - Row, Column Functions
Spreadsheet Formulas - Case States Functions
Spreadsheet Formulas - Date & Time Functions
Spreadsheet Formulas - Numeric Functions
Spreadsheet Formulas - Regular Expression Functions
Regular Expression Overview
Spreadsheet Formulas - Regular Expression Function Examples
Spreadsheet Formulas - Examples
Function Browser, Variable Dialog box
Transformation of Text Variables (Variables of Type Text)
Case Selection Conditions - Overview
Case Selection Conditions - Syntax Summary
Case Selection Conditions - Examples
Spreadsheet Case Selection Conditions
Spreadsheet Case Selection Conditions - Selections Tab
Spreadsheet Case Selection Conditions - Display Tab
Spreadsheet Case Selection Conditions - Subset/Random Sampling Tab
Spreadsheet Case Weights
Verifying Data
Verification Conditions Syntax Summary
Verify Data
Data Verification
Data - Verify Data - Find Next Invalid Case
Standardizing Data
Standardization of Values
Ranking Values
Rank Order Values
Data Filtering/Recoding
Filter Duplicate Cases
Filter Sparse Data
Process Invariant Variables
Recode Outliers and Extreme/Rare Values
Process Missing Data
MD Imputation
Recoding Values - Example
Recode Values of Variable Dialog Box
Date Operations Dialog Box
Date Operations - Numeric Dates Tab
Date Operations - Text Dates Tab
Unstacking/Stacking Dialog Box
Unstacking/Stacking - Unstacking Tab
Unstacking/Stacking - Stacking Tab
Stacking Example
Data - Auto Filter - Auto Filter
Auto Filter Criteria
Top 10 Auto Filter
Data - Auto Filter - Set as Selection Conditions
Data - Auto Filter - Mark Cloaked Cases Excluded
Data - Auto Filter - Mark Cloaked Cases Hidden
ModifyingSpreadsheets
Editing the Contents of a Cell
Variable and Case Metadata Overview
Dialog Boxes
Merge Options
Merge Options - Variables Tab
Merge Options - Text Labels Tab
Merge Options - Cases Tab
Merge Options - Options Tab
Merge Variables Example
Create a Random Sample
Create a Random Sample - Simple Sampling Tab
Create a Random Sample - Stratified Sampling Tab
Create a Random Sample - Options Tab
Change Icon
Find
Go To
Gridlines
Insert Object
Missing Data Replacement
Recalculate Spreadsheet Formulas
Replace
Case State
FormattingSpreadsheets
Variable Text Values
Create a Custom Header or Footer (for Spreadsheets)
Modify Header/Footer
Using Formats
Formats - Overview
Creating a New Format
Applying a Format
Modifying a Format
Renaming a Format
Deleting a Format
Saving a Format
Spreadsheet Formatting Options
Format Characters
Format Dialog Boxes
Format Manager
Format Name
Edit Format Dialog Box
Edit Format
Edit Format - Number Tab
Edit Format - Alignment Tab
Edit Format - Font Tab
Edit Format - Border Tab
Spreadsheet Layouts - Overview
Creating a New Spreadsheet Layout
Apply a Spreadsheet Layout
Edit a Spreadsheet Layout
Rename a Spreadsheet Layout
Delete a Spreadsheet Layout
Spreadsheet Layouts Dialog Boxes
Spreadsheet Layout Manager
Spreadsheet Layouts - General tab
Spreadsheet Layouts - System tab
Edit Spreadsheet Layout
Spreadsheet Layout Name
Case Operations
Adding Cases
Add Cases
Moving Cases
How to Move Before Case 1
Move Cases
Copying Cases
How to Copy Before Case 1
Copy Cases
Deleting Cases
Delete Cases
Case Names
Case Names Manager
Set Case Height
VariableOperations
Adding Variables
Add Variables
Moving Variables
How to Move Before Variable 1
Move Variables
Shift Variables
Copying Variables
Copy Variables
Deleting Variables
Delete Variables
Variable Specifications
Variable Specifications Editor
Text Labels Editor
Variable Bundle Manager
Using the Text Labels Editor
Notes on Text Labels and Text Values
Values/Stats
Sort Options
Set Variable Width
Select Variables
Frequently Asked Questions
General
Can I create a custom list to use for extrapolation?
Can I open more than one input data file simultaneously?
Can I use the results of one analysis to perform another analysis?
Can I create a custom list to use for extrapolation?
What is a matrix spreadsheet?
Does Statistica support drag-and-drop opening of spreadsheets and graphs?
How much disk space is necessary to perform database management operations?
Can I undo editing operations?
What is the difference between the file header and a customized header?
What graphs are available from spreadsheets?
How do I insert an object into the spreadsheet?
How Do I Interpret the Spreadsheet Audit Log File
Cases, Variables, Formats
What are cases and variables?
Why do we need different variable types?
How do I add or delete variables (columns of data)?
How do I add/delete cases (rows of data)?
Can I select (highlight) the entire spreadsheet?
Can I edit the contents of a cell (and not overwrite it)?
How are dates represented in Statistica data files?
How is time represented in Statistica data files?
How do I convert date-values into text values and vice-versa?
How do I review and edit variable specifications (names, formats, notes, formulas)?
How are missing data handled in Statistica?
How do I change the missing data code for individual variables?
How do I edit case names?
How do I create a new cell/block format?
How do I apply a format?
How do I create a new spreadsheet layout?
How do I apply a spreadsheet layout?
What is the difference between hidden and excluded case states?
Text Labels
Can I label numeric values?
How do I enter/edit the assignments between numeric values and text labels?
Can I switch between displaying text labels and numeric values in the data spreadsheet?
Can I copy a set of text labels, numeric values, and descriptions to other variables and files?
Can Clipboard and drag-and-drop operations in the spreadsheet affect values outside the range that is being pasted or dropped?
Operations on Blocks of Values
What are the drag-and-drop facilities?
Can I rearrange blocks of data or ranges of cases and variables in a data file?
What is the difference between copy and copy with headers?
Do Statistica Spreadsheets support split display?
How can I expand a block in the spreadsheet outside the current screen?
Can I expand the data file using drag-and-drop?
Splitting and Merging Files
How do I split a Statistica data file into smaller files?
How do I merge two Statistica data files?
Can I merge the text labels/numeric values from two files?
Formulas, Transformations, Recoding, Verifying, Cleaning Data
How do I calculate (transform) values of a variable?
Can Statistica automatically recalculate all spreadsheet formulas when the data change?
What is the simplest way to recode values of a variable (e.g., split a continuous variable into categories)?
How do I create values of a new variable based on conditions met by other variables?
What syntax can be used to create case selection/verification/recode conditions (or to calculate variable values using spreadsheet formulas)?
How can I verify and "clean" data?
How do I perform a multiple sort?
How do I rank-order values of a variable (replace values with their ranks)?
How do I transpose data (convert cases into variables)?
How do I automatically fill ranges of data in the spreadsheet?
How does the extrapolation of blocks (AutoFill) work?
How do I standardize values in a block?
Statistical Analyses
How do I select variables for an analysis?
Can variables be selected for analyses by highlighting them in the spreadsheet?
How do I select a subset of cases (observations) to be included in an analysis?
How are case selection conditions stored and saved?
Can I select random subsets of data?
What is the quickest way to review basic descriptive statistics for a variable?
What is the quickest way to review a sorted list of all unique values of a variable?
What are statistics of block data?
Can matrix data be used for input instead of raw data?
Import, Export, DDE, OLE
How can I access data from excel and other foreign data files?
How do I export data from Statistica to Excel and other foreign data files?
Can Statistica access data from enterprise databases?
Can Statistica access data from mainframe databases?
Can Statistica process files that are larger than the local hard drive?
Does Statistica support ODBC?
How do I set up OLE links between Statistica and other Windows applications?
How do I set up DDE links between Statistica and other windows applications?
Working with Graphs
UnderstandingGraphs
Graph Window Overview
Graphs in Statistica
Customization of Graphs
Creating Graphs via Statistica Visual Basic
References, Acknowledgments, Standards
General Categories of Graphs
Graphs of Input Data
Graphs of Block Data
Graphs Menu Graphs
User Defined Graphs
Other Specialized Graphs
Graphs Menu Graphs
General Overviews
Graphs in Statistica
2D Graphs
3D Sequential Graphs
3D XYZ Graphs
Conceptual Overviews - Icon Plots
Conceptual Overviews - Matrix Plots
Categorized Graphs
Conceptual Overviews
2D Graphs Overview
Conceptual Overviews - 2D Histograms
Conceptual Overviews - 2D Scatterplots
Conceptual Overviews - 2D Box (and Means with Error) Plots
Conceptual Overviews - 2D Range Plots
Conceptual Overviews - 2D Line Plots (Variables)
Conceptual Overviews - 2D Scatterplot with Error Bars
Bag Plots - Introductory Overview
Conceptual Overviews - Scatter Icon Plots
Conceptual Overviews - Scatter Image Plots
Conceptual Overviews - Normal Probability Plots
Conceptual Overviews - Quantile-Quantile Plots
Conceptual Overviews - Probability-Probability Plots
Conceptual Overviews - Pie Charts
Conceptual Overviews - Sequential/Stacked Plots
Conceptual Overviews - Missing/Range Data Plots
3D XYZ Graphs Overview
Conceptual Overviews - 3D Scatterplots
Conceptual Overviews - Trace Plots
Conceptual Overviews - Surface Plots
Conceptual Overviews - Contour Plots
Conceptual Overviews - Ternary Plots
Conceptual Overviews - 3D Categorized Plots
3D Sequential Graphs Overview
Conceptual Overviews - Raw Data Plots
Conceptual Overviews - Bivariate Histograms
Conceptual Overviews - 3D Range and Box Plots
Conceptual Overviews - Icon Plots
Conceptual Overviews - Matrix Plots
Conceptual Overviews - What Are Categorized Graphs?
Conceptual Overviews - Categorized Histograms
Conceptual Overviews - Categorized Scatterplots
Creating Graphs
2D Graphs Overview 1
2D Graphs
Histograms
2D Histograms
2D Histograms - Quick Tab
2D Histograms - Advanced Tab
2D Histograms - Appearance Tab
2D Histograms - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Specify Boundaries For
Scatterplots
2D Scatterplots
2D Scatterplots - Quick Tab
2D Scatterplots - Advanced Tab
Specify Multiple Subsets
2D Scatterplots - Appearance Tab
2D Scatterplots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Bag Plots
2D Bar/Column Plots
Bag Plot - Quick Tab
Bag Plot - Advanced Tab
Specify Multiple Subsets
Bag Plot - Appearance Tab
Bag Plot - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Means w/Error Plots
Means with Error Plots
Means with Error Plots - Quick Tab
Means with Error Plots - Advanced Tab
Means with Error Plots - Appearance Tab
Means with Error Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Box Plots
2D Box Plots
2D Box Plots - Quick Tab
2D Box Plots - Advanced Tab
2D Box Plots - Appearance Tab
2D Box Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Variability Plots
Variability Plot
Variability Plot - Quick Tab
Variability Plot - Quick Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Range Plots
2D Range Plots
2D Range Plots - Quick Tab
2D Range Plots - Advanced Tab
2D Range Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Scatterplot with Error Bars
Scatterplot with Error Bars
Scatterplot with Error Bars - Quick Tab
Scatterplot with Error Bars - Advanced Tab
Scatterplot with Error Bars - Appearance Tab
Scatterplot with Error Bars - Appearance Tab
Scatterplot with Error Bars - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Scatter Icon Plots
Scatter Icon Plots
Scatter Icon Plots - Quick Tab
Scatter Icon Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Scatter Image Plots
Scatter Image Plots
Scatter Image Plots - Quick Tab
2D/3D Scatter Image Plots - Appearance Tab
2D/3D Scatter Image Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Scatterplots with Histograms
2D Scatterplots with Histograms
2D Scatterplots with Histograms - Quick Tab
2D Scatterplots with Histograms - Advanced Tab
2D Scatterplots with Histograms - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Scatterplots with Box Plots
2D Scatterplots with Box Plots
2D Scatterplots with Box Plots - Quick Tab
2D Scatterplots with Box Plots - Advanced Tab
2D Scatterplots with Box Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Normal Probability Plots
Normal Probability Plots
Normal Probability Plots - Quick Tab
Normal Probability Plots - Appearance Tab
Normal Probability Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Quantile-Quantile Plots
Quantile-Quantile Plots
Quantile-Quantile Plots - Quick Tab
Quantile-Quantile Plots - Advanced Tab
Quantile-Quantile Plots - Appearance Tab
Quantile-Quantile Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Probability-Probability Plots
Probability-Probability Plots
Probability-Probability Plots - Quick Tab
Probability-Probability Plots - Advanced Tab
Probability-Probability Plots - Appearance Tab
Probability-Probability Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Bar/Column Plots
2D Bar/Column Plots
2D Bar/Column Plots - Quick Tab
2D Bar/Column Plots - Advanced Tab
2D Bar/Column Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Line Plots (Variables)
2D Line Plots - Variables
2D Line Plots - Variables - Quick Tab
2D Line Plots - Variables - Advanced Tab
2D Line Plots - Variables - Appearance Tab
2D Line Plots - Variables - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Line Plots (Case Profiles)
2D Line Plots - Case Profiles
2D Line Plots - Variables - Quick Tab
2D Line Plots - Variables - Advanced Tab
2D Line Plots - Case Profiles - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Sequential/Stacked Plots
Sequential/Stacked Plots
Sequential/Stacked Plots - Quick Tab
Sequential/Stacked Plots - Advanced Tab
Sequential/Stacked Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Pie Charts
Pie Charts
Pie Charts - Quick Tab
Pie Charts - Appearance Tab
Pie Charts - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Missing and Out-of-Range Data Plots
Missing and Out-of-Range Data Plots
Missing and Out-of-Range Data Plots - Quick Tab
Missing and Out-of-Range Data Plots - Advanced Tab
Missing and Out-of-Range Data Plots - Appearance Tab
Missing and Out-of-Range Data Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Custom Function Plots
2D Custom Function Plots
2D Custom Function Plots - Custom Function Tab
2D Custom Function Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Sequential Graphs
3D Raw Data Plots
3D Raw Data Plots - Quick Tab
3D Raw Data Plots - Advanced Tab
3D Box Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Bivariate Histograms
3D Bivariate Histograms - Quick Tab
3D Bivariate Histograms - Advanced Tab
3D Bivariate Histograms - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Range Plots
3D Range Plots - Quick Tab
3D Range Plots - Advanced Tab
3D Range Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Box Plots
3D Box Plots - Quick Tab
3D Box Plots - Advanced Tab
3D Box Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D XYZ Graphs
3D Scatterplots
3D Scatterplots - Quick Tab
3D Scatterplots - Advanced Tab
3D Scatterplots - Appearance Tab
3D Scatterplots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Scatter Image Plot
3D Scatter Image Plot - Quick Tab
2D/3D Scatter Image Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Surface Plots - Quick Tab
3D Surface Plots - Appearance Tab
3D Surface Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Contour Plots
3D Contour Plots - Quick Tab
3D Contour Plots - Appearance Tab
3D Contour Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Wafer Plots
3D Wafer Plots - Quick Tab
3D Wafer Plots - Appearance Tab
3D Wafer Plots - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Ternary Graphs
Ternary Graphs - Quick Tab
Ternary Graphs - Advanced Tab
Ternary Graphs - Appearance Tab
Ternary Graphs - Categorized Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Categorized Plots
3D Categorized Plots - Quick Tab
3D Categorized Plots - Advanced Tab
3D Categorized Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Ternary Graphs
Categorized Ternary Graphs - Quick Tab
Categorized Ternary Graphs - Advanced Tab
Categorized Ternary Graphs - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Custom Function Plots
3D Custom Function Plots - Quick Tab
3D Custom Function Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Matrix Plots
Matrix Plots - Quick Tab
Matrix Plots - Advanced Tab
Matrix Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Icon Plots
Icon Plots - Quick Tab
Icon Plots - Advanced Tab
Icon Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Graphs
2D Categorized Histograms
2D Categorized Box Plots - Quick Tab
2D Categorized Box Plots - Advanced Tab
2D Categorized Histograms - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
2D Categorized Scatterplots
2D Categorized Scatterplots - Quick Tab
2D Categorized Scatterplots - Advanced Tab
2D Categorized Scatterplots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Means with Error Plots
Categorized Means with Error Plots - Quick Tab
Categorized Means with Error Plots - Advanced Tab
Categorized Means with Error Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
2D Categorized Box Plots
2D Categorized Box Plots - Quick Tab
2D Categorized Box Plots - Advanced Tab
2D Categorized Box Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Normal Probability Plots
Categorized Missing and Out-of-Range Data Plots - Quick Tab
Categorized Normal Probability Plots - Advanced Tab
Categorized Normal Probability Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Quantile-Quantile Plots
Categorized Quantile-Quantile Plots - Quick Tab
Categorized Quantile-Quantile Plots - Advanced Tab
Categorized Quantile-Quantile Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Probability-Probability Plots
Categorized Probability-Probability Plots - Quick Tab
Categorized Probability-Probability Plots - Advanced Tab
Categorized Probability-Probability Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Line Plots
Categorized Line Plots - Quick Tab
Categorized Line Plots - Advanced Tab
Categorized Means with Error Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Pie Charts
Categorized Pie Charts - Quick Tab
Categorized Pie Charts - Advanced Tab
Categorized Pie Charts - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Missing and Out-of-Range Data Plots
Categorized Missing and Out-of-Range Data Plots - Quick Tab
Categorized Missing and Out-of-Range Data Plots - Advanced Tab
Categorized Missing and Out-of-Range Data Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
3D Categorized Plots
3D Categorized Plots - Quick Tab
3D Categorized Plots - Advanced Tab
3D Categorized Plots - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Categorized Ternary Graphs
Categorized Line Plots - Quick Tab
Categorized Ternary Graphs - Advanced Tab
Categorized Ternary Graphs - Appearance Tab
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Graphs of Block Data - Histogram: Block Columns
Graphs of Block Data - Histogram: Entire Columns
Graphs of Block Data - Line Plot: Entire Columns
Graphs - Graphs of Block Data - Line Plot: Block Rows
Graphs of Block Data - Box Plot: Block Columns
Graphs of Block Data - Normal Probability Plot: Block Columns
Graphs - Graphs of Block Data - Custom Graph from Block by Column
Graphs of Block Data - Custom Graph from Block by Row
Graphs of Block Data - Custom Graph for Entire Column
Graphs - Graphs of Block Data - Custom Graph for Entire Row
Graphs of Block Data - Customize List
Graphs of Input Data - Values/Stats
Graphs of Input Data - Histogram
Graphs - Graphs of Input Data - Histogram - Regular
Graphs - Graphs of Input Data - Histogram - Normal Fit
Graphs of Input Data - Box-Whisker
Graphs - Graphs of Input Data - Box-Whisker - Median/Quart./Range
Graphs - Graphs of Input Data - Box-Whisker - Mean/SE/SD
Graphs - Graphs of Input Data - Box-Whisker - Mean/SD/1.96*SD
Graphs - Graphs of Input Data - Box-Whisker - Mean/SE/1.96*SE
Graphs of Input Data - Probability Plot
Graphs - Graphs of Input Data - Probability Plot - Normal Probability
Graphs - Graphs of Input Data - Probability Plot - Half-Normal Prob
Graphs - Graphs of Input Data - Probability Plot by - Detrended
Graphs of Input Data - Scatterplot by ...
Graphs - Graphs of Input Data - Scatterplot by - Regression, 95% Conf
Graphs - Graphs of Input Data - Scatterplot by - Polynomial Fit, 95% Conf
Graphs - Graphs of Input Data - Scatterplot by - Regular
Graphs - Graphs of Input Data - Scatterplot by - with Histograms
Graphs - Graphs of Input Data - Scatterplot by - with Box Plots
Graphs of Input Data - 2D Histogram by ...
Graphs - Graphs of Input Data - 3D Histogram by - Continuous by Continuous
Graphs - Graphs of Input Data - 3D Histogram by - Discrete by Discrete
Graphs - Graphs of Input Data - 3D Histogram by - Continuous by Discrete
Graphs - Graphs of Input Data - 3D Histogram by - Discrete by Continuous
Graphs of Input Data - 3D Histogram by ...
Graphs - Graphs of Input Data - 3D Histogram by - Continuous by Continuous
Graphs - Graphs of Input Data - 3D Histogram by - Discrete by Discrete
Graphs - Graphs of Input Data - 3D Histogram by - Continuous by Discrete
Graphs - Graphs of Input Data - 3D Histogram by - Discrete by Continuous
Graphs of Input Data - Box-Whisker by ...
Graphs - Graphs of Input Data - Box-Whisker by - Median/Quart./Range
Graphs - Graphs of Input Data - Box-Whisker by - Mean/SE/SD
Graphs - Graphs of Input Data - Box-Whisker by - Mean/SD/1.96*SD
Graphs - Graphs of Input Data - Box-Whisker by - Mean/SE/1.96*SE
Graphs of Input Data - Probability Plot by ...
Multiple Graph Layout
Multiple Graph Layouts - Autolayout Wizard Overview
AutoLayout Wizard - Step 1
AutoLayout Wizard - Step 2
Multiple Graph Layouts - Template Overview
Graph Template
Graph Data Size
Data Size Threshold
Large Data Warning
Size of Statistica Graph Files
Using Graphs
Adding Objects to a Graph
Graph Locking
Merge Graph
Paste Graph
Show Brushing
Polyline Object Properties
Modifying Graphs
Graph Overviews
Advanced Graphics Capabilities
Compound Graph Customization
Extra Objects in Compound Graphs
Mini-Formatting Toolbar
Multiple Graph Layouts - Autolayout Wizard Overview
Multiple Graph Layouts - Template Overview
Menus
File Menu
Edit Menu
View Menu
Insert Menu
Format Menu
Statistics Menu
Graphs Menu
Tools Menu
Window Menu
Help menu
Graph Data Editor - Layouts Menu
Graph Data Editor - Layouts Tab/Menu
Layouts - Graph Options
Layouts - Plot Layout
Layouts - Calculated Graph Data
Layouts - Fit Values
Voronoi Values
Layouts - Custom Function Values
Layouts - Point Label/ID Info
Layouts - Brushing Mark
Layouts - Brushing Label
Layouts - Brushing Exclude
Layouts - Brushing Hide
Brushing Off
Brushing On
Layouts - Reset (Brushing) Block
Data Selected by Brushing Menu
Data Selected by Brushing
Data Selected by Brushing - Copy to Clipboard
Data Selected by Brushing - Copy To New Plot
Data Selected by Brushing - Move To New Plot
Data Selected by Brushing - Delete
Data Selected by Brushing - De-select
Shortcut Menus
Graph Shortcut Menus
Axis Properties
Bar Options
Box and Whisker Style
Capture Rectangle
Capture Rectangle
Clone Graph
Regression Bands
Convert to Floating Text
Convert To Footnote
Convert To Left Title
Convert To Right Title
Convert To Subtitle
Convert To Title
Convert To Title (Submenu Command)
Copy Graph
Copy Object
Custom Units
Cut Object
Delete Object
Disconnect Text from Graph
Ellipse
Fitting
General
General Plot Options
Graph Data Editor
Graph Options
Hide
Histogram
Lock Object
Major Units
Minor Units
Paste
Pattern
Plot Properties
Point Labels
Print Graph
Properties
Save Graph
Scale Values
Scaling
Screen Catcher
Select Axis
Set Document Size and Scaling
Set Graph Region
Show All Titles/Floating Text
Text Object Properties
Title
Title Properties
Graph Data Editor Shortcut Menus
Add Plot
Graph Options
Brushing Highlight
Brushing Exclude
Brushing Exclude - Toggle Exclude Block
Brushing Exclude - Include Block
Brushing Exclude - Exclude Block
Brushing Label
Brushing Label
Brushing Label - Toggle Label Block
Brushing Label - Label Block
Brushing Label - Unlabel Block
Brushing Mark
Brushing Mark
Brushing Mark - Toggle Mark Block
Brushing Mark - Mark Block
Brushing Mark - Unmark Block
Calculated Graph Data
Copy
Custom Function Values
Cut
Data Selected by Brushing
Data Selected by Brushing
Data Selected by Brushing - Copy to Clipboard
Data Selected by Brushing - Copy To New Plot
Data Selected by Brushing - Move To New Plot
Data Selected by Brushing - Delete
Data Selected by Brushing - De-select
Fit Values
Plot Layout
Point Label/ID Info
Reset (Brushing) Block
Turn Off
Brushing Hide
Brushing Hide - Toggle hide Block
Brushing Hide - Hide Block
Brushing Hide - Show Block
Dialog Boxes
Document Size and Scaling
Graphs Gallery
Graph Options
Specify Multiple Subsets
Summary of Settings
Lock Graph
Animation
Axis Options
Axis: General (2D Graphs)
Axis: General (3D Graphs)
Axis: Title
Axis: Scaling
Axis: Major Units
Axis: Minor Units
Axis: Scale Values
Axis: Custom Units
Axis: Reference Lines
Vertical Axis
Copy Axis Specs To
Box/Whisker
Box/Whisker More Options
Spreads
Brushing
Brushing 2D/3D
Brushing 2D/3D - Interactive Tab
Brushing 2D/3D - Extended Tab
Categorization
Categorization
Category Overlay Areas
Category Overlay Fit Lines
Category Overlay Markers
Category Overlay Lines
Embedded Object
Arrow Object Properties
OLE Object Properties
Shape Object Properties
Snap to Grid
Text Object Properties
Function
Custom Function (2D Graphs)
Custom Function  (3D XYZ Graphs)
Custom Function (Ternary Plots)
Custom Function
Edit 3D Custom Function
Edit Custom Function
Function Name Manager
Graph Layout
Graph: Layout (2D Graphs)
Graph: Layout (3D Sequential Graphs)
Graph: Layout (Icon Plots)
Graph: Layout (Matrix Plots)
Icon Plot
Icon Scatterplots
Chernoff Face Definition
Patterns (Icon Plots)
Line, Area, and Marker
Area Properties
Line Properties
Marker Properties
Picture Markers - Graphics File Images
Menu Customization
Customize Graph Menu
Customize User Graph Menu
Options
2D/3D Graphs - Options 1 Tab
2D/3D Graphs - Options 2 Tab
Graph Options
Number Formatting
Value Format
Number Format
Pie Chart
Multiple Pies
Plot
Plot: Aggregation
Plot: Bars
Plot: Box/Whisker
Plot: Classes
Plot: Ellipse
Plot: Fitting
Plot: Fitting (3D XYZ Graphs)
Plot: General
Plot: General (3D Sequential Graphs)
Plot: General (3D XYZ Graphs)
Plot: Histogram
Plot: Missing Data
Plot: Normal Probability
Plot: Pies
Plot: Point Labels
Plot: Point Labels (3D Sequential Graphs)
Plot: Point Labels (3D XYZ Graphs)
Plot: Point Labels (Icon Plots)
Plot: PP
Plot: QQ
Plot: Regression Bands
Plot: Scatterplot
Plot: Spreads
Plot: Ternary
Plot: Variability Plot
New Plot(s)
Related to Plot
Select Graph
Select Graph
Select Graph - 2D Graphs
Select Graph - 3D Sequential Graphs
Select Graph - 3D XYZ Graphs
Select Graph - Icon Graphs
Select Graph - Matrix Graphs
Statistics
Statistics (2D Histogram Plots)
Statistics (Bivariate Histogram Plots)
Statistics (Box-Whisker Type Plots)
Statistics (for Scatterplot-Type Plots)
Statistics (Normal Probability Plots)
Style
New Style
Surface
Surface
Surface Specifications
3D Surface Plots
Titles & Legend
Add Legend
Graph Titles/Text
Title
User-Defined Graph
Specify Name for User-defined Graph
New User-Defined Graph
Customization of Graphs
Customizing Graphs: Default Settings
Interactive Graph Customization
Compound Graphs
Creating User Defined Compound Graphs
Compound Graph Customization
Compound\Multiple Graphs
Graph Data Editor
Graph Data Editor Overview
Calculated Graph Data
Function/Fit Values
Voronoi Values
Graph Data Point Info
Menus
Layouts
Graph Data Editor - Layouts Tab/Menu
Layouts - Graph Options
Layouts - Plot Layout
Layouts - Calculated Graph Data
Layouts - Fit Values
Layouts - Custom Function Values
Layouts - Point Label/ID Info
Layouts - Brushing Mark
Layouts - Brushing Label
Layouts - Brushing Hide
Layouts - Brushing Exclude
Layouts - Reset (Brushing) Block
Data Selected by Brushing
Data Selected by Brushing
Data Selected by Brushing - Copy to Clipboard
Data Selected by Brushing - Copy To New Plot
Data Selected by Brushing - Move To New Plot
Data Selected by Brushing - Delete
Data Selected by Brushing - De-select
Shortcut
Add Plot
Cut
Copy
Calculated Graph Data
Fit Values
Custom Function Values
Point Label/ID Info
Brushing Mark
Brushing Mark
Brushing Mark - Toggle Mark Block
Brushing Mark - Mark Block
Brushing Mark - Unmark Block
Brushing Label
Brushing Label
Brushing Label - Toggle Label Block
Brushing Label - Label Block
Brushing Label - Unlabel Block
Turn Off
Brushing Hide
Brushing Hide - Toggle hide Block
Brushing Hide - Hide Block
Brushing Hide - Show Block
Brushing Highlight
Brushing Exclude
Brushing Exclude - Toggle Exclude Block
Brushing Exclude - Include Block
Brushing Exclude - Exclude Block
Reset (Brushing) Block
Data Selected by Brushing
Data Selected by Brushing
Data Selected by Brushing - Copy to Clipboard
Data Selected by Brushing - Copy To New Plot
Data Selected by Brushing - Move To New Plot
Data Selected by Brushing - Delete
Data Selected by Brushing - De-select
Formatting Graphs
Using Graphics Styles
Graphics Styles - Overview
Graphics Styles - Contents of Graphics Styles
Graphics Styles - Managing and Editing Graphics Styles
Graphics Styles - Applying a Graphics Style
Graphics Styles - Importing and Exporting Styles
Graph Import Warning
Creating a Graphics Style
Graphics Styles
List of Styles
Style Properties
Graphics Styles - Examples
Example 1: Styles that Apply to the Entire Graph
Example 2: Adding Styles for Graph Objects
Example 3: Styles for Individual Features
Document Styles - Overview
Creating a Document Style
Frequently Asked Questions
General
What categories of graphs are available?
Are there different customization options for each type of graph?
Can graphs be automatically updated when the data file changes?
In what formats can I save graphs?
What is the Windows metafile graphics format?
What is the bitmap graphics format?
What is the JPEG graphics format?
What is the PNG (Portable Network Graphics) format?
What is the native Statistica graphics format?
How do I export a Statistica graph to another application?
What is the difference between a graph and a plot?
What are categorized graphs?
How do I define categories in categorized graphs?
How do I produce ternary contour plots and surfaces?
How do I produce graphs with polar coordinates?
What are multiple axes in graphs?
How is the mouse used in graph applications?
How do I select objects in a graph?
How can I interpret a 100(1-alpha)% confidence interval?
Graph Customization
General Features
When and how can I customize Statistica graphs?
How do I add a new plot to an existing graph?
What is the Graph Options dialog?
How can I adjust the margins of a graph?
How can I change the proportions of the graph window?
How do I produce sequences of graphs from lists of variables?
How do I specify properties for point markers?
How do I specify area properties?
How do I specify line properties?
Can I control the resolution of fit lines?
Styles
What are graph styles?
How can I create a style from a custom graph title format?
How can I apply a saved style to a graph title in a new graph?
Does altering the graph defaults on the Options dialog affect graph styles?
If I save a graph with customized styles, how will this graph appear on a colleague's computer who does not have those particular styles?
Can I transfer a graph style from one system to another?
What do the letter icons represent in the graph styles manager?
Scales
How do I customize the layout and format of an axis?
How do I replace numeric scale values with text labels?
Can I insert a scale break?
Can I shift the position of axes against the graph?
Can I adjust the number of minor units?
Can I specify custom locations for tickmarks?
What is the difference between manual and auto scaling?
Titles, Legends, Custom Text
Is all graph text editable?
Can I customize the location and format of the legend?
What other types of legends are automatically created in graphs?
How can I add a title to a graph?
Can I enter a symbol into a graph title?
Can I convert the standard titles into movable text?
How do I place a graph title or a footnote in a fixed position?
How do I rotate text?
Fitting, Plotting Functions
How do I fit a function to data?
How do I display a specific equation for the fitted function?
How do I plot a custom-defined function?
How do I fit a custom-defined function to data?
How do I plot a parametric curve?
Interactive Analysis
Brushing
What is the Graph Data Editor?
Is there a glossary of brushing terms?
How can data points selected via brushing be identified and managed in the Graph Data Editor?
How can brushing be used in exploratory data analysis?
How can brushed data points be identified and managed in the Graph Data Editor?
What is animated brushing?
Can I interactively review points that belong to specific plots in multiple scatterplots and other multiple graphs?
How can I identify specific subsets of data in a graph?
Can I interactively identify data points in a graph?
How can I use the slices brush?
3D Displays
Can I rotate or adjust the perspective of a 3D graph?
How do I perform exploratory spinning of 3D data displays?
How do I change the axis proportions in 3D graphs?
How do I define a custom selection of levels for a contour plot or surface plot?
How do I define a custom palette for a contour plot or surface plot?
Can I 3D zoom on a selected cube?
Compound Graphs, Embedding, Clipboard, Multiple Graph Management
How can I place one Statistica graph into another?
How can I place a foreign graph or artwork into a Statistica graph?
How do I place text in a Statistica graph?
What are compound graphs?
Can I represent objects in graphs as expandable icons?
How can I copy an entire Statistica graph to the Clipboard?
How can I copy a selected part of a Statistica graph to the Clipboard?
Can I create a blank graph?
Can I place multiple graphs on one page?
What is the Multiple Graph AutoLayout Wizard?
How can I undo operations on objects?
Printing Graphs
Are fonts set to specific sizes always printed having the requested physical sizes?
Are there options to make printed graphs look better?
Can I quickly adjust sizes of all fonts in a graph?
Do all printer drivers support rotated fonts?
Do all printers support the non-transparent overlaying of graphic objects?
What is the difference between the dynamic and fixed status of custom objects (e.g., arrows, custom text, drawings) in graphs?
Hidden and Excluded Case States
What is the difference between hidden and excluded case states?
Examples
Example: Creating a Wafer Plot
Example: Creating a Graph of Input Data
Example: Creating a Graph of Block Data Using an Input Data File
Example: Creating a Graph of Block Data Using an Analysis Output Spreadsheet as Input
Example: Creating and Customizing a Simple 2D Graphs Menu Graph
Specifying Multiple Subsets Example
Graphics Styles - Examples
Example 1: Styles that Apply to the Entire Graph
Example 2: Adding Styles for Graph Objects
Example 3: Styles for Individual Features
Notes and Technical Information
2D Fit Lines
3D Graph Surfaces
Assessing the Fit of a Distribution via Quantile-Quantile Plots
Axis Overview
Calculating Contour Levels and Distance
Categorization Methods
Categorized Scatterplots - Overview
Distance-Weighted Least Squares Fitting
Distributions
Probability-Probability Plots
Beta Distribution for Probability-Probability Plots
Exponential Distribution for Probability-Probability Plots
Extreme Value (Gumbel) Distribution for Probability-Probability Plots
Gamma Distribution for Probability-Probability Plots
Lognormal Distribution for Probability-Probability Plots
Normal Distribution for Probability-Probability Plots
Rayleigh Distribution for Probability-Probability Plots
Weibull Distribution for Probability-Probability Plots
Quantile-Quantile Plots
Beta Distribution for Quantile-Quantile Plots
Exponential Distribution for Quantile-Quantile Plots
Extreme Value (Gumbel) Distribution for Quantile-Quantile Plots
Gamma Distribution for Quantile-Quantile Plots
Lognormal Distribution for Quantile-Quantile Plots
Normal Distribution for Quantile-Quantile Plots
Rayleigh Distribution for Quantile-Quantile Plots
Weibull Distribution for Quantile-Quantile Plots
Fitting Options for Ternary Plots
Fitted Functions for Histograms
Mark Selected Subsets
Maximum Likelihood Method for Q-Q and P-P Plots
Method of Categorization
Method of Categorization (or Intervals)
Method of Categorization - Boundaries
Method of Categorization - Categories
Method of Categorization - Codes
Method of Categorization - Integer Mode
Method of Categorization - Unique Values
Method of Categorization - Multiple Subset
Mini-Formatting Toolbar
Negative Exponentially-Weighted Fitting
Outliers and Extremes
Parametric Curve
Spline Fitting
Suppressing Scale Values Near Scale Breaks
Working with Workbooks
Workbook Overview
What Is ActiveX?
ActiveX Documents
Analysis Workbook Overview
Multi item Display Overview
Input Spreadsheet Overview
Creating Workbooks
Inserting Files to a Workbook
Opening a Workbook
Workbook Tree Overview
Workbook Tab Character Length
Menus
File Menu
View Menu
Statistics Menu
Graphs Menu
Tools Menu
Working with Workbooks
Workbook - Insert
Workbook - Delete
Workbook - Rename
Workbook - Extract as Stand-alone Window - Copy
Workbook - Extract as Stand-alone Window - Original
Workbook - Save Item(s) As
Workbook - Cut Workbook Document
Workbook - Copy Workbook Document
Workbook - Paste As Workbook Document - As First Child
Workbook - Paste As Workbook Document - As Last Child
Workbook - Paste As Workbook Document - As Next Sibling
Workbook - Paste As Workbook Document - As Prior Sibling
Workbook - Use as Active Input
Workbook Item Print Orientation
Workbook - Tab Control - Bottom (One Row)_New
Workbook - Tab Control - Top (One Row)
Workbook - Tab Control - Left (Multiple Rows)
Workbook - Tab Control - Visible
Workbook - Tab Control - Tab Length
Workbook - Multi-Item Display - Refresh
Workbook - Multi-Item Display - Copy
Workbook - Multi-Item Display - Select All
Workbook - Multi-Item Display - Settings
Workbook - Multi-Item Display - Default Settings
Workbook - Lock User Interface
View - Events - View Code
View - Events - Autorun
View - Events - Password
Window Menu
Help menu
Workbook Tree - Drag-and-Drop
Workbook Tree - Cancel
Workbook Tree - Copy After
Workbook Tree - Copy As Child
Workbook Tree - Copy Before
Workbook Tree - Move After
Workbook Tree - Move As Child
Workbook Tree - Move Before
Copy Workbook Document
Cut Workbook Document
Delete
Export Graphs As - Pictures - File
Export Graphs As - Objects - New PowerPoint Presentation
Export Graphs As - Objects - New Word Document
Export Graphs As - Pictures - File
Export Graphs As - Pictures - New PowerPoint Presentation
Export Graphs As - Pictures - New Word Document
Extract as Stand-Alone Window - Original
Extract as Stand-Alone Window - Copy
Insert
Paste as Workbook Document - As First Child
Paste as Workbook Document - As Last Child
Paste as Workbook Document - As Next Sibling
Paste as Workbook Document - As Prior Sibling
Properties
Rename
Use as Active Input
Inserting Files to a Workbook
Extracting a File from a Workbook
Adding Items to a Workbook
Insert Workbook Item
Adding the Current Item to a Workbook
Adding the Current Item to a Workbook
Adding a Previously Saved Object to a Workbook
Adding a Folder to a Workbook
Adding a Document to a Workbook
Document Type
Adding a New Statistica Document to a Workbook
Adding a Saved Statistica Document to a Workbook
Adding a New ActiveX Document to a Workbook
Saving a Workbook
Printing a Workbook
Workbook Header/Footer
Workbook Item Print Orientation
Custom Header and Footer (for a Workbook)
Global Workbook Options
Workbook Drag-and-Drop Features
Rearranging the Workbook Tree with Drag-and-Drop
Using Copy and Paste in a Workbook
Workbook Clipboard Features
Renaming Workbook Items
Deleting Items from a Workbook
Statistica Workbook
FAQ
Can I add a folder to a workbook?
Can I print more than one item from a workbook without printing the entire workbook?
Can I rearrange the workbook tree with drag-and-drop?
Can I select discontinuous items in the workbook tree?
How do I add a new ActiveX document to a workbook?
How do I add a new Statistica document to a workbook?
How do I add a previously saved object to a workbook?
How do I add a saved Statistica document to a workbook?
How do I add the current item to a workbook?
How do I delete items from a workbook?
How do I rename workbook items?
How do I use copy and paste in a workbook?
Working with Reports
Report Overview
Report Tree Overview
Report Window Overview
Analysis Report Overview
CreatingReports
Creating a New Report
Adding Items to a Report
UsingReports
Opening a Report
Saving a Report
Menus
File Menu
Edit Menu
View Menu
Insert Menu
Format Menu
Statistics Menu
Graphs Menu
Tools Menu
Window Menu
Help menu
Shortcut
Center
Extract - Copy
Extract - Original
Font
Insert Documents
Left Justify
Object Properties
Properties
Rename
Right Justify
Show Tree
DIALOGS
Insert Documents
Insert Object
Links
Paragraph Format
Output Options
Tabs
Printing a Report
Report Print Layout
Using the Print Preview Window
Create a Custom Header or Footer
Custom Header and Footer (for a Workbook)
ModifyingReports
Global Report Options
Renaming Report Items
Rearranging Items in a Report with Drag-and-Drop
Using Cut, Copy, and Paste in a Report
Frequently Asked Questions
Can I append output from multiple sessions to the same report?
Can I combine text, tables, and graphs in one report?
Can I create a custom header or footer for my report?
Can I rename an item in a report?
Can I save my reports in HTM format?
Can I use Clipboard functions to rearrange items in the report tree?
Can I use drag-and-drop to rearrange items in the report tree?
How can I automatically print (or save to reports) all spreadsheets and/or all graphs from an analysis?
How can I print text/graphics reports from analyses?
How do I adjust margins in the reports?
How do I create a new report?
What is the difference between RTF and STR (Statistica Report) format?
What supplementary information is available with analyses and how can I add it to my reports?
Working with Macros (Statistica Visual Basic)
Statistica Visual Basic (SVB) Overviews
Macro (Statistica Visual Basic) Overview
Macro (SVB) Programs Window Overview
Statistica Visual Basic (SVB) Programming Environment
A Simple Message Box, and If..Then..End If Block
Performing Computations, Data Types, Subroutines, Functions
Collections vs. Arrays
The Variant Data Type
Global Variables, Passing Arguments By Value (ByVal) or By Reference (ByRef)
Objects, Methods, and Properties
The Statistica VB Object Model
A Simple Statistica Visual BASIC (SVB) Program
Macro (SVB) Programs Example - Retrieving a Collection of Spreadsheets
Macro (SVB) Programs Example - Accessing Results Spreadsheet Data
Statistica Libraries and Modules
Accessing Statistica Visual Basic Libraries
Macro (SVB) Programs Example - Accessing Multiple Modules in a Single Program
Speeding Up the Execution (Loading) of Statistica Visual Basic Programs by Removing Unnecessary References (Type Libraries)
Recording Simple Macro (SVB) Programs, Documenting an Analysis
Example - Recording a Simple Macro
Common Elements of Recorded Macro (SVB) Programs
Editing and Customizing Recorded Macro (SVB) Programs
Macro (SVB) Programs Example - Statistica Dialog Box for Selecting Data Files
Using Arrays of Values to Set the .Variables Property
Macro (SVB) Programs Example - Statistica Dialog Boxes for Selecting Variables
Macro (SVB) Programs Example - Customizing a Simple Descriptive Statistics Macro
Error Handling
Opening New Applications, Opening Files, Printing
Macro (SVB) Programs Example - Launching a New Statistica Application
Macro (SVB) Programs Example - Opening and Saving Statistica Documents
Macro (SVB) Programs Example - Printing Statistica Documents
Basic Functions for Editing Objects (Spreadsheets, Graphs, etc.)
Macro (SVB) Programs Example - Formatting Cells in Spreadsheets
Macro (SVB) Programs Example - Copying Cells from a Spreadsheet into a Report
Macro (SVB) Programs Example - Creating a Custom Spreadsheet
Macro (SVB) Programs Example - Customizing Graphs
Macro (SVB) Programs Example - Customizing Reports
Macro (SVB) Programs Example - Customizing Workbooks
Macro (SVB) Programs Example - Customizing Toolbars and Menus via Statistica Visual Basic
Macro (SVB) Programs Example - Adding a Custom Toolbar via a SVB Macro Program
Statistica Matrix and Statistical Function Libraries
Include File STB.svx and Statistica Visual Basic Library of Matrix Functions
Include File Graphics.svx
A Simple Example - Inverting a Matrix
Example - Accessing All Data in Spreadsheets via the .Data Property
Example - Accessing Variables (Columns) in Spreadsheets via the .VData(i) Property
Calling Statistica from Visual Basic in Other Applications (Excel, Word, etc.)
Macro (SVB) Programs Example - Accessing Statistica Visual Basic Functions from Microsoft Excel
Macro (SVB) Programs Example - Accessing Statistica Visual Basic Functions from Microsoft Word
General SVB Documentation
General Visual Basic Syntax Documentation - Interactive WinWrap Help Engine
Basic Rules for Simple SVB Programs
Statistica Visual Basic Syntax Reference - User-defined Functions and Subroutines
Variable and Function Naming Conventions
Declaring a Variable
Variables and Values of Variables
How Do I Assign a Value to a Variable?
Statistica Visual Basic Syntax Reference - User-defined Functions and Subroutines
Statistica Visual Basic Syntax Reference - User-defined Functions and Subroutines (Arguments)
Global Variables, Passing Arguments By Value (ByVal) or By Reference (ByRef)
How Do I Set (or Leave Unchanged) Default Values of Function Parameters?
How to Specify Arrays in Statistica Visual Basic Functions
Array Indexing
Redimensioning an Array
How Do I Specify a Conditional Instruction?
Advanced Conditional Expressions
Controlled Loops
Conditional Loops
Example - Recording an Analysis Macro
New Macro
Common Elements of Recorded Macro (SVB) Programs
Example - Recording a Simple Macro
Example - Recording a Simple Macro with Modal Dialog Specifications
Macro Type
Defining Class Modules and Object Modules
Debugging a Macro Program
Error Handling
Debugging/Logging
General Help Features of the Statistica VB Environment
Function Browser
Object Browser
References
Speeding Up the Execution (Loading) of Statistica Visual Basic Programs by Removing Unnecessary References (Type Libraries)
Calling Functions in External DLLs
How to use Strings in Statistica Visual Basic
Retrieving Lists of Strings in Statistica Visual Basic
Passing Lists of Strings to Statistica Visual Basic Functions
Macro (SVB) Program Examples
Are There Example Statistica Visual Basic Programs Included in the Package?
A Simple Statistica Visual BASIC (SVB) Program
A Simple Message Box, and If..Then..End If Block
Macro (SVB) Programs Example - Basic Spreadsheet Manipulation
Macro (SVB) Program Examples - Creating a Random Sized Spreadsheet
Macro (SVB) Programs Example - Resize a Spreadsheet
Macro (SVB) Programs Example - Auto-Fitting a Spreadsheet's Variable Columns
Macro (SVB) Programs Example - Converting a Spreadsheet's Data Type
Macro (SVB) Programs Example - Creating Sample Statistics (Descriptives and Correlations)
Macro (SVB) Programs Example - Creating a Custom Spreadsheet
Macro (SVB) Programs Example - Formatting Cells in Spreadsheets
Macro (SVB) Programs Example - Accessing Results Spreadsheet Data
Macro SVB Programs Examples - Searching a Spreadsheet's Cells for a Value
Macro (SVB) Programs Example - Inserting an Array into a Spreadsheet
Macro (SVB) Programs Example - Retrieving a Collection of Spreadsheets
Macro (SVB) Programs Example - Customizing a Simple Descriptive Statistics Macro
Macro (SVB) Programs Example - Accessing Multiple Modules in a Single Program
Macro (SVB) Programs Example - Creating a Cell-Function Spreadsheet
Macro (SVB) Programs Example - Creating a Self-Updating Dataset
Graphs
Macro (SVB) Programs Example - Creating and Customizing Box-Plots
Macro (SVB) Programs Example - Accessing and Customizing Graphs
Macro (SVB) Programs Example - Creating User-Defined Custom Functions
Macro (SVB) Programs Example - Customizing Graphs
Macro (SVB) Programs Example - Creating and Customizing Graph Objects
Macro (SVB) Programs Example - Graph Function Library
Reports
Macro (SVB) Programs Example - Customizing Reports
Macro (SVB) Programs Example - Copying Cells from a Spreadsheet into a Report
Workbooks
Macro (SVB) Programs Example - Customizing Workbooks
Macro (SVB) Programs Example - Inserting All Open Windows into a Workbook
Statistica Interface
Macro (SVB) Programs Example - Launching a New Statistica Application
Macro (SVB) Programs Example - Adding a Custom Toolbar via a SVB Macro Program
Macro (SVB) Programs Example - Opening and Saving Statistica Documents
Macro (SVB) Programs Example - Printing Statistica Documents
Macro (SVB) Programs Example - Statistica Dialog Box for Selecting Data Files
Macro (SVB) Programs Example - Statistica Dialog Boxes for Selecting Variables
Macro (SVB) Programs Example - Writing a Custom Statistica Application with User-Defined Dialog Boxes
Macro (SVB) Programs Example - Displaying a Progress Bar
Statistica Query
Macro (SVB) Program Examples - Creating a Statistica Query Toolbar
Macro (SVB) Program Examples - Running a Query via a Macro
Other Environments
Macro (SVB) Programs Example - Accessing Statistica Visual Basic Functions from Microsoft Excel
Limitations and Differences between SVB and Visual Basic in Other Applications
CustomDialogs
Custom Dialog Boxes; Custom User Interfaces
Servicing Complex Dialog Boxes via Dialog Functions (DialogFunc)
Servicing Option Buttons, Options Buttons, List Boxes, etc.
Macro (SVB) Programs Example - Writing a Custom Statistica Application with User-Defined Dialog Boxes
A Simple Dialog Box in Statistica Visual Basic
Dialogs
UserDialog Editor
Object Browser
Function Browser
Grid Settings
References
Controlling Statistica Events with SVB Programs
Macro Event Behavior
Document Events
Document-Level Events Example
Spreadsheet Document-Level Events
Workbook Document-Level Events
Graph Document-Level Events
Report Document-Level Events
Events Warning
Application-Level Events
Application-Level Events Example
Events
Analysis-Level Events Example
Document-Level Events Example
Analysis-Level Events Example
Macro (SVB) Programs Example - Creating a Cell-Function Spreadsheet
Macro (SVB) Programs Example - Creating a Self-Updating Dataset
Recording Macros
Recording Simple Macro (SVB) Programs, Documenting an Analysis
Common Elements of Recorded Macro (SVB) Programs
Recording Macros - Automatic Programming
Recording Analysis Output
Recording Case Selection Conditions and Case Weights
Recording Data File Selections
Recording Data Editing Operations
Master Macros
Placing Results in Workbooks, Reports, Etc., via the RouteOutput Method
Error Handling of Recorded Macros
Analysis Warnings
Non-Fatal Errors (Warnings) that Occur
Recording Macro Examples
Example - Recording a Simple Macro
Example - Recording an Analysis Macro
Example - Recording a Simple Macro with Modal Dialog Specifications
Matrix Library of SVB Functions
Statistica Visual Basic Matrix Language - Overview
Statistica Visual Basic Library of Matrix Functions
Accessing the Matrix Library via the MatrixObject
A Simple Example - Inverting a Matrix
Matrix Functions
Statistica Visual Basic Library of Matrix Functions - Dim Array_Name (XDim, ...)
Statistica Visual Basic Library of Matrix Functions - LOWESS (VectorX, VectorY, SmoothF, Nsteps, Delta, VectorXSorted, VectorYSmooth, VectorRWeights, VectorResiduals)
Statistica Visual Basic Library of Matrix Functions - MatrixCombineVert (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixAdd (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixAllNonZero (Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixAnyNonZero (Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixCholeskyDecomposition (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixCombineHoriz (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixCopy (SrcMatrix, SrcRow, SrcCol, ?NoOfRows, ?NoOfCols, DestMatrix, DestRow, DestCol)
Statistica Visual Basic Library of Matrix Functions - MatrixCorrelations (Matrix, Intercept, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixCrossProductOfDev (Matrix1, Intercept, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixDet (Matrix, Result)
Statistica Visual Basic Library of Matrix Functions - MatrixDisplay (Matrix, ?Title$)
Statistica Visual Basic Library of Matrix Functions - MatrixDuplicate (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixEigenValues (Matrix, Eigenvalues, NumEigen)
Statistica Visual Basic Library of Matrix Functions - MatrixEigenVectors (Matrix, Eigenvalues, Eigenvectors, NumEigen)
Statistica Visual Basic Library of Matrix Functions - MatrixElemAdd (Matrix, Scalar, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixElemDivide (Matrix, Scalar, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixElemMultiply (Matrix, Scalar, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixElemSubtract (Matrix, Scalar, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixExtract (Matrix, Row, Column, ?NoOfRows, ?NoOfCols, SubMatrix)
Statistica Visual Basic Library of Matrix Functions - MatrixFill (Scalar, DestMatrix, DestRow, DestCol, ?NoOfRows, ?NoOfCols)
Statistica Visual Basic Library of Matrix Functions - MatrixGeneralPseudolnverse (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixGetColumn (Matrix, Column, ColVector)
Statistica Visual Basic Library of Matrix Functions - MatrixGetDiagonal (Matrix, DiagVector)
Statistica Visual Basic Library of Matrix Functions - MatrixGetRow (Matrix, Row, RowVector)
Statistica Visual Basic Library of Matrix Functions - MatrixGramSchmidtOrt (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIdentity (Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixInverse (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsEqual (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsGreater (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsGreaterEqual (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsLess (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsLessEqual (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixIsNotEqual (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixKroneckerMultiply (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixLogicalAnd (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixLogicalNot (Matrix1, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixLogicalOr (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixMeans (Matrix, Vector)
Statistica Visual Basic Library of Matrix Functions - MatrixMultiply (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixRank (Matrix, Result)
Statistica Visual Basic Library of Matrix Functions - MatrixRead (FileName$, Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixReadFromDataFile (FileName$, ?FirstCase, ?FirstVar, ?NoOfCases, ?NoOfVars, Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixSetColumn (Matrix, Column, ColVector)
Statistica Visual Basic Library of Matrix Functions - MatrixSetDiagonal (Matrix, DiagVector)
Statistica Visual Basic Library of Matrix Functions - MatrixSetRow (Matrix, Row, RowVector)
Statistica Visual Basic Library of Matrix Functions - MatrixSetToZero (Matrix)
Statistica Visual Basic Library of Matrix Functions - MatrixSingularValues (Matrix, SingularValues)
Statistica Visual Basic Library of Matrix Functions - MatrixSingularValuesDecomp (Matrix, U, W, V)
Statistica Visual Basic Library of Matrix Functions - MatrixSolve (Matrix, MatrixSolution, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixSubtract (Matrix1, Matrix2, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixSumOfSquares (Matrix, Result)
Statistica Visual Basic Library of Matrix Functions - MatrixSweep (Matrix, From, To, Dir)
Statistica Visual Basic Library of Matrix Functions - MatrixTrace (Matrix, Result)
Statistica Visual Basic Library of Matrix Functions - MatrixTranspose (Matrix, MatrixResult)
Statistica Visual Basic Library of Matrix Functions - MatrixWrite (FileName$, Matrix)
Statistica Visual Basic Library of Matrix Functions - NewScrollsheet (?NoOfRows, ?NoOfColumns, Array, ?Title$, ?RowNames$, ?ColumnNames)
Statistica Visual Basic Library of Matrix Functions - ReDim Array_Name (XDim, ...)
Statistica Visual Basic Library of Matrix Functions - ScrollsheetGetMatrix (Handle, Row, Column, Matrix)
Statistica Visual Basic Library of Matrix Functions - ScrollsheetSetMatrix (Handle, Row, Column, Matrix)
Statistica Visual Basic Library of Matrix Functions - VectorDualSort (Vector, IndexVector, Direction)
Statistica Visual Basic Library of Matrix Functions - VectorRank (Vector, Direction, DupControl)
Statistica Visual Basic Library of Matrix Functions - VectorSort (Vector, Direction)
Statistica Visual Basic Library of Matrix Functions - Wishart
Menus
File Menu
Edit Menu
View Menu
Debug
Debug - Step Into
Debug - Step Over
Debug - Step Out
Debug - Run To Cursor
Debug - Add Watch
Debug - Quick Watch
Debug - Toggle Breakpoint
Debug - Clear All Breakpoints
Debug - Set Next Statement
Debug - Show Next Statement
Run
Run - Run Macro
Run - Break
Run - Reset
Statistics Menu
Graphs Menu
Tools Menu
Workbook Menu
Window Menu
Help menu
Importing STB/SCL
Importing Version 5.x STB Programs to Statistica Visual Basic (Technical Notes)
Importing Version 5.x SCL Programs to Statistica Visual Basic (Technical Notes)
STB Import Warning
SCL Import Warning
Migrating from StaDev to Statistica's COM Architecture
StaDev Functions
StaAddCases
StaAddLabel
StaAddVars
StaCloseFile
StaCreateFile
StaDeleteCases
StaDeleteLabel
StaDeleteLabelForValue
StaDeleteVars
StaGetAllMD
StaGetCaseData
StaGetCaseName
StaGetCaseNameLength
StaGetData
StaGetFileHeader
StaGetLabelForValue
StaGetLongLabelForValue
StaGetNCases
StaGetNumVarLabels
StaGetNVars
StaGetValueForLabel
StaGetVarFormat
StaGetVarLabelByIndex
StaGetVarLongName
StaGetVarMD
StaGetVarName
StaOpenFile
StaSetCaseData
StaSetCaseName
StaSetCaseNameLength
StaSetData
StaSetFileHeader
StaSetVarFormat
StaSetVarLongName
StaSetVarMD
StaSetVarName
Statistica Programming in C++ Overview
Exporting a Spreadsheet as Text in C++
Running Basic Statistics in C++
Creating a Statistica Add-In
Installing an Add-In
ProgID
Statistica Programming in C# Overview
Creating and Editing a Box & Whisker Plot in C#
Statistica Programming in VB.NET Overview
Sending Custom Statistics to Statistica in VB.NET
Statistica Programming in Delphi Overview
Running Descriptive Statistics in Delphi
FAQ
General Introduction to Statistica Visual Basic
What is Statistica Visual Basic (SVB)?
What are some of the applications of SVB?
I know nothing about Visual Basic or programming; where do I start?
How do I access SVB?
What does a simple SVB program look like?
What is meant by the term "object model?"
What are properties, what are methods?
Recording Macros Recording an Analysis into a Statistica Visual Basic Program
How can I record my analysis in an SVB program?
How can I record an Analysis Macro?
What exactly is recorded in Analysis Macros?
How can I record a Master Macro (Log of Analyses)?
What exactly is recorded in Master Macros?
How do I execute an SVB program from within Statistica?
Components of Recorded Macros SVB Programs Case Selection Conditions Output Options
How are case selection conditions and case weights handled in recorded macros?
How are output options handled in recorded macros?
How can I speed up the execution (loading) of Statistica Visual Basic programs by removing unnecessary references (type libraries)?
What are the major "components" in a typical SVB macro program?
What are the names for the Statistica reference libraries (modules)?
General Statistica Visual Basic Language Features Data Types Subroutines Functions Classes Objects
Which data types are supported in SVB?
How do I assign a value to a variable?
What are collections?
How do I specify a conditional instruction (if ... then)?
How do I execute an expression in a loop?
I already know about Visual Basic; Does SVB support advanced language features?
What are subroutines and functions?
What is the difference between passing variables to subroutines and functions by value and by reference?
What are local variables vs. global variables?
Can I define my own class modules and object modules?
Can I expand my SVB by calling external DLLs?
Statistica Visual Basic Development Environment
Does the SVB environment provide tools for debugging?
How can I get on-line help?
What is the Function Browser?
How are errors handled during the execution of an SVB program?
How are non-fatal errors (warnings) handled during the execution of an SVB program?
Does SVB include a "Dialog Painter"?
Using SVB To Manage Statistica Analyses DataFiles Results Spreadsheets Graphs
How is SVB integrated with Statistica data (input), analyses and output?
How can I access results spreadsheets, graphs, workbooks, etc.?
How can I create a blank graph, and set data directly into the graph?
What is the RouteOutput method; what is the AnalysisOutput object?
Which kind of output can I create with SVB?
How can I change the font (or other aspects) of numbers in spreadsheets?
How can I modify the appearance of graphs (e.g., axis labels) from within SVB?
How can other aspects of graphs be modified?
Can I use SVB to develop "extensions" of Statistica and my own new "modules?"
How can I attach a macro program to a toolbar button (keyboard command, or menu option)?
Running Statistica Visual Basic Programs from Other Applications
Can I execute an SVB program from within other Visual Basic compatible applications?
How can I run a Statistica analysis from Microsoft Excel?
Handling Large Computational Problems Matrix Operations
What is the capacity of SVB, and can it handle large computational problems?
Does SVB support matrix operations?
How can I use the data in a spreadsheet with matrix functions? (How do I copy data efficiently from spreadsheets to matrices, and back?)
Creating and Managing Custom Dialogs in Statistica Visual Basic
Can I create custom dialogs and other interactive user input controls in SVB?
How are the different controls in custom dialogs "serviced?"
Customizing the Appearance and Behavior of Statistica via Statistica Visual Basic
What are application events and how can they be controlled from SVB?
How can I create a cell-function spreadsheet using spreadsheet events?
Can I customize the toolbars and menus via SVB?
Working with Statistica Query
Query Overview
Statistica Query - Quick, Step-by-Step Instructions
Statistica Query - Streaming of Data on Remote Servers
Streaming database connector technology (technical overview)
Streaming database connector FAQs
Graphic Mode vs. Text Mode
Graphic Mode Overview
Field Sequence Tab
Criteria Tab
SQL Statement Tab
Preview Data Tab
Text Mode Overview
Additional Functionality Available in Text Mode
Join Overview
Join Operators
Criteria Overview
Criteria Operators
Select Database Query
Table - Add
Query Properties
Global Options
Query Options
Database Connection
Edit Database Connection
Add Join
Edit Join
Add Criteria
Select Value(s)
Edit Criteria
Returning External Data to Spreadsheet
External Data Range Properties
Query - Retrieving Data
Query File Menu
File - New
File - Open
File - Save
File - Save As
File - Return Data to Statistica
File - Cancel and Return to Statistica
Query Edit Menu
Edit - Undo
Edit - Cut
Edit - Copy
Edit - Paste
Edit - Delete
Edit - Select All
Edit - Query Properties
Edit - Global Options
Query View Menu
View - Toolbar
View - Status Bar
View - Graphic Mode
View - SQL and View - Cube/MDX (Beta)
View - Refresh Preview Data
View - More Preview Data
View - Automatically Preview Data
View - Suppress Blank MDX Data
Query Table Menu
Table - Add
Table - Remove
Table - Remove All
Query Join Menu
Join - Add
Join - Remove
Join - Edit
Join - Remove All
Query Criteria Menu
Criteria - Add
Criteria - Remove
Criteria - Remove
Criteria - Edit
Criteria - Remove All
Query Help Menu
Help - Contents and Index
Help - About Statistica Query
Example - Querying a Database Using ODBC
Example - Querying a Database Using OLE DB
Can I save a query and use it later on another data set?
Can I use more than one query on a data file?
How do I add joins or criteria to my query?
How do I retrieve external data via Statistica Query?
How do I set up a database connection using the OLE DB provider for ODBC?
How do I set up a database connection using the Statistica spreadsheet OLE DB provider?
What is SQL?
What is the difference between graphic mode and text mode in Statistica Query?
What is the difference between ODBC and OLE DB?
Command Line Options Overview
General Parameters
Debug Logging
Installation/Startup
Statistica Enterprise Related Parameters
Statistica Projects Overview
Statistica Projects FAQ
Save as Statistica Project
Open Statistica Project
File - Close Project
Customizing Statistica
Customizing Statistica Overview
Customizing Statistica - Keyboard
Customizing Statistica - Macros
Customizing Statistica - Toolbars and Menus
Customizing Statistica - Interactive User Interface
Customizing Statistica - Documents
Customizing Statistica - Local vs. Permanent Customizations
Customizing Statistica - General Defaults
Customizing Statistica - Graphs
Maintaining Different Configurations of Statistica
Customized Configurations for Individual Users on a Network
Customizing Statistica - Toolbar Overview
Graphs Toolbar
2D Histogram Button
2D Scatterplot Button
2D Means with Error Plots Button
Box Plot Button
Range Plot Button
Scatter Icon Plots Button
Scatter Image Plots Button
2D Scatterplots with Histograms Button
2D Scatterplots with Box Plots Button
Probability Plot Button
Quantile-Quantile Plot Button
Probability-Probability Plot Button
Click the Bar/Column Plot Button
2D Line Plot Button
2D Line / Case Profiles Button
2D Stacked Plot Button
Pie Chart Button
Missing Data / Range Plot Button
3D Raw Data Plot Button
3D Histogram Button
3D Range Plot Button
3D Box Plot Button
3D Scatterplot Button
3D Scatter Image Plots Button
3D Surface Button
3D Contour Button
3D Ternary Plot Button
Matrix Plots Button
Icon Plots Button
Categorized Histogram Button
Categorized Scatterplot Button
Categorized Means with Error Plots Button
Categorized Box Plot Button
Categorized Probability Plot Button
Categorized Quantile-Quantile Plot Button
Categorized Probability-Probability Plot Button
Categorized Line Plot Button
Categorized Pie Chart Button
Categorized Missing Data / Range Plot Button
Categorized 3D Plot Button
Categorized 3D Ternary Plot Button
Graph Tools Toolbar
Graphic Styles Box
Brushing Tool Button
3D Rotation Control Button
Display Graph Fit to Window Button
Display Graph at Actual Size Button
Adjust the Graph Actual Size/Scaling Button
Set Graph Area Button
Snap to Grid Button
Zoom In Button
Zoom Out Button
Zoom Pan Button
Reset Zoom Button
Selection Tool Button
Text Button
Rectangles Button
Rounded Rectangles Button
Ellipse Button
Arcs Button
Polygon Button
Arrow, Custom Bar Button
Insert Object Button
Line Pattern/Color Button
Area Pattern/Color Button
Move to Front Button
Move to Back Button
Increase Font Button
Decrease Font Button
Statistics Toolbar
Resume Button
Macro Button
Basic Statistics/Tables Button
Multiple Regression Button
ANOVA/MANOVA Button
Nonparametric Statistics Button
Distribution Fitting Button
Cluster Analysis Button
Factor Analysis Button
Principal Components and Classification Analysis Button
Canonical Analysis Button
Reliability/Item Analysis Button
Classification Trees Button
Correspondence Analysis Button
Multidimensional Scaling Button
Discriminant Analysis Button
General Discriminant Analysis Button
General Linear Models Button
Generalized Linear/Nonlinear Models Button
General Regression Models Button
Partial Least Squares Button
Variance Components Button
Survival Analysis Button
Nonlinear Estimation Button
Fixed Nonlinear Regression Button
Log-Linear Analysis Button
Time Series Button
SEPATH Button
Quality Control Charts Button
Process Analysis Button
DOE Button
Six Sigma Button
Power Analysis Button
General Slicer/Dicer Explorer with Drill-Down Button
General Classifier (Trees and Clusters) Button
General Modeler and Multivariate Explorer Button
General Forecaster Button
General Neural Network Explorer Button
General Classification/Regression Trees Button
CHAID Button
Generalized Additive Models Button
Standard Toolbar
New File Button
Open File Button
Save File Button
Print Button
Print Preview Button
Copy (Block) Button
Copy (Block) Button
Paste (Block) Button
Format Painter Button
Undo Button
Redo Button
Find Button
Replace Button
Add to Workbook Button
Add to Report Button
Help Topics Button
Context Sensitive Help Button
Six Sigma Toolbar
Define Button
Measure Button
Analyze Button
Improve Button
Control Button
Six Sigma Calculator Button
Status Bar
Progress Bar
Message Area
Name Box
Show Field
Case Selection Conditions
Case Weights
Caps Lock
Num Lock
Macros Recording Status
Spreadsheet Toolbar
Font Name and Font Size
Character Formatting Buttons
Paragraph Formatting Buttons
Format Cells Button
Font Color Button
Fill Color Button
Format Menu Button
Show/Hide Text Labels Button
Show/Hide Case Names Button
Increase Decimal Button
Decrease Decimal Button
Show/Hide Marked Cells Button
Show Selection Conditions Button
Selection Conditions On/Off Button
Define Weight Button
Sort Button
Recalculate Spreadsheet Formulas Button
Global Operations on Variables Button Menu
Global Operations on Cases Button
Tools Toolbar
Mark (Selected Cells) Button
Unmark (Selected Cells) Button
New Mark Button
Unmark All Button
Select - Include Button
Select - Exclude Button
New Selection Condition Include Button
Report Toolbar
Font Name and Font Size
Character Formatting Buttons
Paragraph Formatting Buttons
Font Color Button
Fill Color Button
Font Dialog Button
Macro Toolbar
User Dialog Button
Object Browser Button
Function Browser
Run Macro Button
Pause Button
Reset Button
Step Into Button
Step Over Button
Step Out Button
Run to Cursor Button
Add Watch Button
Quick Watch Button
Breakpoint Toggle Button
Clear All Breakpoints Button
Set Next Statement Button
Record Macro Button
Statistica Query Toolbar
New Query Button
Open Query Button
Save Query Button
Cut Button
Copy Button
Paste Button
About Button
Help Topics Button
Context Sensitive Help Button
Return Data to Statistica Button
Switch Mode Button
Graph Data Editor Toolbar
Open the Graph Options Dialog Button
Open the Plot Layout Dialog Button
Display Calculated Graph Data Associated with this Plot Button
Add a New Plot Button
Toggle Mark Data Points (Brushing) Button
Mark Data Points (Brushing) Button
Unmark Data Points (Brushing) Button
Toggle Label Data Points (Brushing) Button
Label Data Points (Brushing) Button
Unlabel Data Points (Brushing) Button
Toggle Data Points (Temporarily) On/Off Button
Turn Off (Temporarily) Data Points Button
Turn Back On Data Points Button
Toggle Highlight Data Points (Brushing) Button
Highlight Data Points (Brushing) Button
Dehighlight Data Points (Brushing) Button
Reset Data Points (Brushing) Button
Customizing Statistica - Menus Overview
File Menu
Edit Menu
View Menu
Insert Menu
Format Menu
Layouts Menu
Statistics Menu
Data Mining Menu
Graphs Menu
Tools Menu
Workbook Menu
Data Menu
Window Menu
Help menu
Customizing Statistica - Application Frame Menus
Customizing Statistica - Shortcut Menus
Options Menu
Customizing Statistica - Commands Overview
Getting Started
Create a New Toolbar
Customizing Toolbars
Customizing Menus
Toolbar and Menu Appearance
Assign/Remove Keyboard Shortcut Keys
Customize
Customize - Command/Macros Tab
Customize - Toolbars Tab
Toolbar Name
Customize - Menu Tab
Customize - Keyboard Tab
Customize - Options Tab
Button Appearance
Edit Button Image
Options
Options - General
Options - Analyses/Graphs: Display
Options - Analyses/Graphs: Limits
Options - Analyses/Graphs: Output Manager
Options - Documents/Spreadsheets: Navigation/Defaults
Options - Documents/Spreadsheets: Warnings
Options - Documents/Graphs: Display
Options - Documents/Graphs: Settings
Options - Documents/Graphs: Rendering
Options - Documents: Workbooks
Options - Documents: Macros
Options - Documents: Reports
Options - Documents: Workspaces
Options - Documents: Streaming DB Connector
Options - Configurations
Options - Custom Lists
Options - Import
Options - Server/Web
Options - SharePoint
Options - R Integration
Customize - Background Tab
Options - Windows
About Statistica
About Statistica - Analytic Facilities
About Statistica - Unique Features
About Statistica - The General Philosophy of the Statistica Approach
About Statistica - Software Technology
Web Enablement - Statistica Enterprise Server
About Statistica - Record of Recognition
Document Types
Features of Analyses
Introductory example - correlations
Statistica Decisioning Platform
Weight of Evidence (WoE) Overview
Weight of Evidence (WoE)
WoE Settings
Interaction Terms
Custom Split Groups
Customize Groups for a Categorical Variable
Add/Delete Variables after Computing Groups
Weight of Evidence (WoE) Technical Notes
Weight of Evidence (WoE) example
Rules Builder Overview
Rules Builder
Edit Parameters
Text Syntax
Edit If Condition
Edit Formula
Choose Model
Import Rules
Push Array
Array Min Max
Edit Comment
Overview: Batch Processing Using Workflows and Statistica Enterprise Server
Enterprise Systems
Statistica Enterprise
Statistica Data Miner
Web Enablement - Statistica Enterprise Server
Knowledge Portal (an Interactive Output Portal)
Data Mining with Statistica
Auto-Updating (Running) Workspace Projects (example, When the Data Change)
Controlling the Flow of Data through Nodes: Some Special Issues and Considerations
Data Mining with Statistica Data Miner
Data Mining Overview
Data Mining Very Large Data Sets (Databases): Scalability of Statistica Data Miner
Data Warehousing
Exploratory Data Analysis (EDA) and Data Mining Techniques
Getting Started with Statistica Data Miner
Global Dictionary
How to Write Custom Workspace Nodes
On-Line Analytic Processing (OLAP)
Statistica Data Miner Summary
Structure and user interface of Statistica Data Miner
The Client-Server Version of  Statistica Data Miner and Data Mining via Statistica Enterprise Server
Using Statistica Data Miner with Extremely Large Data Sets
Using C/C++, C#, and Java Code for Deployment
Working with Text Variables and Text Values: Ensuring Consistent Coding
Data Mining - Workspaces Menu Commands
View
View - Show Node Identifiers
View - Align Icons
Run
Run - Run All Nodes
Run - Run Modified Nodes
Run - Run to Selected Node
Run - Stop
Nodes Menu
Nodes - Node Browser
Nodes - Choose Input Data Source
Nodes - Rename
Nodes - New Connection
Nodes - Variable Selection
Nodes - View Document
Nodes - Edit Code
Nodes - Edit Parameter Values
Nodes - Disable Selected Connection
Nodes - Embed Document on Save
Nodes - Review Description
Nodes - Open Input Document Read-Only
Tools
Tools - Node Browser
Tools - Wizard
Tools - Global Dictionary - Edit Global Dictionary
Tools - Global Dictionary - Clear Global Dictionary
Tools - Clear Text Values
Statistica Workspace Toolbar
Statistica Workspace
Node Browser
New Browser
Browser Options
Select Spreadsheet or In-Place DB Document
Select Dependent Variables and Predictors
Select Dependent Variables and Predictors - Quick Tab
Select Dependent Variables and Predictors - Advanced Tab
Large Variable List for Input
Add New Parameters
Edit Parameters
Edit Text
Enter a Name for the New Node
Connect Embedded Data to a Disk File
Warning: Ensure Consistent Coding for Deployment
Data Miner Nodes Dialogs
Statistics
Descriptive Statistics
Correlation Matrices
t-Test, Independent, by Groups
t-Test for Independent Samples, by Variables
t-Test, Dependent Samples
t-Test, Single Sample
Frequency Tables
Breakdown and One-Way ANOVA
Breakdown Non-Factorial Tables
Crosstabulation Tables
Stub and Banner Tables
Testing Differences: r, Percent, Mean
User-Defined 2 x 2 Tables
Standard Multiple Regression
Stepwise Multiple Regression
Main Effects ANOVA
Factorial ANOVA
Repeated Measures ANOVA
Observed vs. Expected Chi-Square
Correlations (Spearman, Kendall Tau, Gamma)
Comparing Two Independent Samples (Groups)
Comparing Multiple Indep. Samples (Groups)
Comparing Two Dependent Samples (Variables)
Comparing Multiple Dep. Samples (Variables)
Distribution Fitting
Advanced Linear and Nonlinear Models
General Linear Models
Main Effects Linear Models
Factorial ANCOVA MANCOVA Models
Repeated Measure Models
Response Surface and Mixture Models
Generalized Linear Models
Generalized Linear and Nonlinear Models
Stepwise and Best Subset Probit Regression
Stepwise and Best Subset Logit Regression
Generalized Linear Models
General Regression Models
Best-Subset and Stepwise Regression
Best-Subset and Stepwise ANCOVA
General Best-Subset and Stepwise Regression
General Partial Least Squares Models
PLS Regression Models
General PLS Models
NIPALS Algorithm (PCA/PLS)
NIPALS Algorithms Deployment
NIPALS Algorithm for PCA
NIPALS Algorithm for PLS
Variance Components
Survival Analysis
Life Tables and Distributions
Kaplan-Meier Product-Limit Method
Comparing Survival in Two Groups
Comparing Survival in Multiple Groups
Regression Models
Nonlinear Estimation
Quick Logit Regression
Quick Probit Regression
User Specified Regression, Least Squares
User Specified Regression and Loss Function
Log-Linear Analysis
Time Series and Forecasting
Time Series Plots
Single-Series Transformations (x=f(x))
Two-Series Transformation (x=f(x,y))
Differencing, Time Series Transformations
Smoothing Transformations
Simple Fourier-Type Transformations
Autocorrelations and Crosscorrelations
Distributed Lags Analysis
Exponential Smoothing
ARIMA Models
Interrupted ARIMA
Single Series Spectral (Fourier) Analysis
Two Series Spectral (Fourier) Analysis
Seasonal Decomposition (Census I)
X11/Y2K Census Method II Monthly
X11/Y2K Census Method II Quarterly
Structural Equation Modeling
Multivariate Exploratory Techniques
Cluster Analysis
K-Means Clustering
Tree Clustering (Joining)
Two-Way Joining Clustering
Factor Analysis
Principal Components and Classification Analysis
Canonical Analysis
Reliability and Item Analysis
Classification Trees
Classification from Ordered Predictors
Classification from Categorical and Ordered Predictors
Exhaustive (C and RT) Search for Univariate Splits
Correspondence Analysis (CA)
Multiple Correspondence Analysis (MCA)
Multidimensional Scaling
Standard Discriminant Analysis
Stepwise Discriminant Analysis
General Discriminant Analysis Models
Best-Subset and Stepwise GDA ANCOVA
General Best-Subset and Stepwise Discriminant Analysis
Industrial Statistics and Six Sigma
Quality Control Charts
X and MR Chart for Variables, SixGraph Summary
X-Bar and R or S Chart for Variables, SixGraph summary
MA X-Bar and R or S Chart for Variables
EWMA X-Bar and R or S Chart for Variables
Attribute Charts (C, U, Np, p)
CuSum Chart for Observations
Pareto Chart
Multiple Stream Process (MSP) X-Bar and R or S Group Control Chart (GCC)
Multiple Stream Process (MSP) X and MR Chart for Variables
Multiple Stream Process (MSP) Attribute Charts (C, U, Np, p)
Hotelling T-Square Control Chart (HTS)
PLS, PCA, Multivariate Batch SPC
Multivariate Statistical Processing Control Deployment
Principal Component Analysis
Partial Least Squares
Batch-Wise Multi-Way Partial Least Squares
Time-wise Multi-way Principal Component Analysis
Time-Wise Multi-Way Partial Least Squares
Process Analysis
Process Capability Analysis
Generate Gage R and R Design
Analyze Gage R and R Experiments
Gage Linearity
Sampling Plans
Weibull and Reliability/Failure Time Analysis
Weibull Probability Paper
Cause-Effect (Ishikawa, Fishbone) Diagrams
Experimental Design (DOE)
2**(K-p) Standard Design Creation
2-Level Screening (Plackett-Burman) Design Creation
2**(k-p) Max. Unconfounded and Min. Aberration Design Creation
Mixed 2 and 3 Level Design Creation
3**(K-p) and Box-Behnken Design Creation
Central Composite Design Creation
Latin Square Design Creation
Taguchi Robust Design Creation
Mixture Design Creation
D- and A- (T-) Optimal Algorithmic Design Creation
Analysis of 2**(K-p) and Screening Designs
Analysis of Mixed 2 and 3 Level Designs
Analysis of 3**(K-p) and Box-Behnken Designs
Analysis of Central Composite Designs
Analysis of Latin Square Designs
Analysis of Taguchi Robust Designs
Analysis of Mixture Designs
Variance Estimation and Precision
Power Analysis
Power Computations
Power Calculation, One Mean, t-Test
Power Calculation, Two Means, t-Test, Independent Samples
Power Calculation, Two Means, t-Test, Dependent Samples
Power Calculation, Several Means, Planned Contrast
Power Calculation, Several Means, ANOVA, 1-Way
Power Calculation, Several Means, ANOVA, 2-Way
Power Calculation, One-Variance, Chi-Square test
Power Calculation, Two Correlations, Z-Test
Power Calculation, Two Proportions, Z-Test
Power Analysis for Survival Analysis, Exponential with Accrual & Dropouts (Power Calculation)
Power Analysis for Structural Equation Modeling (Power Calculation)
Sample Size Computations
Sample Size Calculation, One Mean, t-Test
Sample Size Calculation, Two Means, t-Test, Independent Samples
Sample Size Calculation, Two Means, t-Test, Dependent Samples
Sample Size Calculation, Several Means, Planned Contrast
Sample Size Calculation, Several Means, ANOVA, 1-Way
Sample Size Calculation, Several Means, ANOVA, 2-Way
Sample Size Calculation, One-Variance, Chi-Square Test
Sample Size Calculation, Two Correlations, Z-Test
Sample Size Calculation, Two Proportions, Z-Test
Power Analysis for Survival Analysis, Exponential with Accrual & Dropouts (Sample Size Calculation)
Power Analysis for Structural Equation Modeling (Sample Size Calculation)
Interval Estimation
Interval Estimation, One Mean, t-Tests
Interval Estimation, Two Means, t-Test, Independent Samples
Interval Estimation, Two Means, t-Test, Dependent Samples
Interval Estimation, Several Means, Planned Contrast
Interval Estimation, Several Means, ANOVA, 1-Way
Interval Estimation Calculation, Several Means, ANOVA, 2-Way ANOVA
Power Analysis for Structural Equation Modeling (Interval Estimation Calculation)
TextMiner
Data-Mining
Independent Component Analysis
Independent Component Analysis Deployment
Generalized Cluster Analysis
Generalized K-Means cluster analysis
Generalized EM Cluster Analysis
Association Rules
General Classification And Regression Tree Models
Standard Classification Trees (C&RT)
General Classification Trees (C&RT)
Standard Regression Trees (C&RT)
General Regression Trees (C&RT)
General CHAID Models
Standard Classification CHAID
Exhaustive Classification CHAID
General Classification CHAID
Standard Regression CHAID
Exhaustive Regression CHAID
General Regression CHAID
Advanced C and RT, CHAID (using Interactive Trees)
Advanced Classification Trees (C And RT)
Advanced Regression Trees (C And RT)
Advanced Classification CHAID
Advanced Regression CHAID
Boosting Trees
Boosting Classification Trees
Boosting Regression Trees
Random Forest
Random Forest Classification
Random Forest Regression
Generalized Additive Models
GAM: Normal, Gamma, Poisson
Generalized Additive Logit Models
MARSplines
Machine Learning
Support Vector Machine
Naive Bayes
K-Nearest Neighbors
Rapid Deployment
Goodness of Fit
Goodness of Fit for Multiple Inputs
Feature Selection and Root Cause Analysis
Combining Groups
Combining Groups Regression with Deployment
Combining Groups Classification with Deployment
Graphs
2D Graphs
2D Histograms
2D Scatterplots
2D Means with Error Plots
2D Box Plots
2D Variability Plots
2D Range Plots
2D Scatter Icon Plots
2D Scatterplots with Histograms
2D Scatterplots with Box Plots
2D Normal Probability Plots
2D Quantile-Quantile Plots
2D Probability-Probability Plots
2D Bar Column Plots
2D Line Plots (Variables)
2D Line Plots (Case Profiles)
2D Sequential Stacked Plots
2D Pie Charts
2D Missing and Range Data Plots
3D Sequential Graphs
Raw Data Plot
Bivariate Histograms
3D Range Plots
3D Box Plot
3D XYZ Graphs
Scatterplots
3D Surface Plots
3D Contour Plots
3D Ternary Plots
3D Categorized XYZ Plots
Categorized 3D Ternary Plots
Matrix Plots
2D Icon Plots
Categorized Graphs
Categorized Histograms
Categorized 2D Scatterplots
Categorized Means with Error Bars Plots
Categorized Box Plots
Categorized Normal Probability Plots
Categorized Quantile-Quantile Plots
Categorized Probability-Probability Plots
Categorized 2D Line Plots (Variables)
Categorized Pie Charts
Categorized 3D Ternary Plots
3D Categorized XYZ Plots
Categorized Missing/Range Data Plots
Input Data and Data Acquisition
Input Generator Node
Multiple Copies of Data Source
Data Cleaning and Filtering
Min-Max Data Filtering
Random Sample Filtering
Systematic Random Sampling
Stratified Random Sampling
Replace Missing Data
Feature Selection and Variable Screening
User-Defined Subset
Variable Screening Template
Analyze Var Lists & Determine Categorical Vars
Separate Variable Lists
Data Transformation
Transpose
Sorting
Ranking
Shift
Delete Variables and Cases
Standardize
Transformations of Variables
Stack Variables
Unstack Variables
Recoding Groups
TextMiner
General Slicer, Dicer, and Explorer
Descriptive Statistics
Frequency Tables
Breakdown and One-Way ANOVA
Breakdown Non-Factorial Tables
Crosstabulation Tables
Stub and Banner Tables
Categorized Histograms
Categorized 2D Scatterplots
Categorized Box Plots
Categorized 2D Line Plots (Variables)
Categorized Missing/Range Data Plots
Categorized Means with Error Bars Plots
Categorized Pie Charts
Classification and Discrimination
Split Input Data into Training and Testing Samples (Classification)
Best-Subset and Stepwise GDA ANCOVA with Deployment
Standard Classification Trees with Deployment (C&RT)
Standard Classification CHAID with Deployment
Exhaustive Classification CHAID with Deployment
Boosting Classification Trees with Deployment
Logit Regression with Deployment
Probit Regression with Deployment
Multilayer Perceptron with Deployment (Classification)
Radial Basis Function with Deployment (Classification)
MARSplines For Classification With Deployment
Support Vector Machine with Deployment (Classification)
Naive Bayes with Deployment (Classification)
K-Nearest Neighbors with Deployment (Classification)
Combining Groups Classification with Deployment
Compute Overlaid Lift Charts from All Models
Compute Best Predicted Classification from all Models
Compute Best Predicted Classification with Prediction Probabilities from all Models
Clear all Deployment Info (Classification)
Regression Modeling and Multivariate Exploration
Split Input Data into Training and Testing Samples (Classification)
Standard Multiple Regression with Deployment
Best-Subset and Stepwise ANCOVA with Deployment
Standard Regression Trees (C&RT) with Deployment
Standard Regression CHAID with Deployment
Exhaustive Regression CHAID with Deployment
Boosting Regression Trees with Deployment
Random Forest Regression with Deployment
Multilayer Perceptron with Deployment (Classification)
Radial Basis Function with Deployment (Regression)
MARSplines for Regression with Deployment
Support Vector Machine with Deployment (Regression)
K-Nearest Neighbors with Deployment (Regression)
Combining Groups Regression with Deployment
Compute Best Predicted Classification from all Models
Clears All Deployment Info (Regression)
General Forecaster and Time Series
Create/Filter Single-Series Data Sources
Simple Exponential Smoothing and Forecasting
Exponential Smoothing and Forecasting
ARIMA Models and Forecasting
Display Forecasters
Neural Network Architectures
Linear
Multilayer Perceptron
Radial Basis Function (RBF)
Generalized Regression Neural Network
Clustering Network
Principal Components Network
Probabilistic Neural Network
Probability Calculator
Beta Probability Calculator
Cauchy Probability Calculator
Chi-Square Probability Calculator
Correlation Probability Calculator
Extreme Value Probability Calculator
Exponential Probability Calculator
F Probability Calculator
Gamma Probability Calculator
Laplace Probability Calculator
Logistic Probability Calculator
Log-Normal Probability Calculator
Normal Probability Calculator
Probability Calculator
Rayleigh Probability Calculator
Six Sigma Calculator
Student's t Probability Calculator
Weibull Probability Calculator
Comparing and Merging Multiple Data Sources
Merge Variables
Merge Text Labels
Merge Cases
Interpretation of Variable Lists
How Variable Lists (from the Input Descriptor) are Interpreted in the Analytic Nodes
Statistics Variable Lists
Graphs Variable Lists
Examples
Example 1: A simple project for creating standard reports and summaries
Example 2: Visual Data Mining
Example 3: Predictive data mining and deployment for a continuous output variable
Example 4: Predictive Data Mining for Categorical Output Variable (Classification)
StatisticaTM StatisticaTM Live Score
Execute External Workspace Node
Statistica PI Add-In Overview
PI Add-In Overview
PI Compressed Data
PI Interpolated Data
PI Timed Data
Summary Data
PI Snapshot Data
Select Date
PI Asset Framework Data
Compressed Data
Interpolated Data
Timed Data
Summary Data
Start Snapshot Data
Stop Snapshot Data
Automation Methods
PixxUI.IsLicensed
PixxUI.License
PixxUI.PICompressedData
PixxUI.PIInterpolatedDataItvl
PixxUI.PIConnection
PixxUI.PIInterpolatedDataItvl
PixxUI.PIInterpolatedDataObs
PixxUI.PISnapshotData
PixxUI.PISummaryData
PixxUI.PITimedData
PixxUI.PISummariesDataDuration
PixxUI.PISummariesDataObs
ETL Overview
Data Alignment: Time Indexed vs ID Based
Statistica ETL: Time-Indexed Startup panel and Quick Tab
Statistica ETL: Time-Indexed - Advanced Tab
Statistica ETL: Time-Indexed - Options Tab
Select Data Sources
Select Documents
Select Variables for ETL Time Indexed Analysis
Variable Specification for ETL Time Indexed Analysis
Statistica ETL: ID Based Startup Panel and Quick Tab
Statistica ETL: ID Based Advanced Tab
Select Data Sources
Select Documents
Select Variables for ETL Time Indexed Analysis
Variable Specification for ETL ID Based Analysis
Time Variable Specs
User Specified Intervals
Example 1: Aligning Data From a Cyclone Furnace
Example 2: Comparing Historical Stock prices
Example 3: Aligning Multiple Stages of Process Data
Spotfire Statistica® Data Connectors
Spotfire Documentation and Support Services
Legal and Third-Party Notices