• About Data Science Textbook
  • 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
  • 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)
      • 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
      • 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
      • Frequency Tables - Purpose
      • Frequency Tables - Arrangement of Data
      • Frequency Tables - Applications
    • 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
      • Interpreting the Banner Table
      • 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
        • Statistics Based on Ranks - Spearman R
        • Statistics Based on Ranks - Kendall's Tau
        • Statistics Based on Ranks - Somers D: d(X|Y), d(Y|X)
        • Statistics Based on Ranks - Gamma
        • Statistics Based on Ranks - Uncertainty Coefficients
    • 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
  • Boosted Trees Overview
    • Boosted Trees for Regression and Classification Overview (Stochastic Gradient Boosting) - Basic Ideas
    • Boosted Trees - Computational Details
  • Canonical Analysis Overview
    • General Ideas
    • Sum Scores
    • Canonical Roots/Variates
    • Number of Roots
    • Extraction of Roots
    • Computational Methods and Results
    • Assumptions
  • 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
  • Cluster Analysis Overview
    • Statistical Significance Testing
    • Area of Application
    • Clustering Methods
      • Two-Way Joining
      • k-Means Clustering
      • Joining (Tree Clustering) - Introductory Overview
        • Hierarchical Tree
        • Distance Measures
        • Amalgamation or Linkage Rules
  • Correspondence Analysis Overview
    • Correspondence Analysis - Program Overview
    • Correspondence Analysis - Supplementary Points
    • Multiple Correspondence Analysis (MCA)
    • Correspondence Analysis Introductory Overview - Burt Table
  • Cox Proportional Hazards Model Overview
    • Computational Details Overview
    • Time Dependent Covariates Overview
  • Data Mining Overview
    • Exploratory Data Analysis (EDA) and Data Mining Techniques
    • Neural Network Overview
  • 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
    • Distribution Fitting Overview
      • Distribution Fitting Introductory Overview - Fit of the Distribution
      • Distribution Fitting Introductory Overview - Types of Distributions
  • Distributions & Simulation Overview
  • Elementary Concepts
    • Commonly Used Statistical Tests
  • 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
  • Factor Analysis Overview
    • Factor Analysis as a Classification Method - 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
  • Feature Selection and Variable Screening Overview
    • Feature Selection and Variable Screening - Computational Details
  • Fixed Nonlinear Regression Overview
  • General ANOVA/MANOVA Overview
    • ANOVA/MANOVA Introductory Overview - Basic Ideas
      • Basic Ideas - The Partitioning of Sums of Squares
      • Basic Ideas - Multi-Factor ANOVA
      • Basic Ideas - Interaction Effects
    • ANOVA/MANOVA Introductory Overview - Complex Designs
      • Complex Designs - Between Groups and Repeated Measures
      • Complex Designs - Incomplete (Nested) Designs
    • Multivariate Designs - Between-Groups Designs
      • Multivariate Designs - Repeated Measures Designs
      • Multivariate Designs - Sum Scores versus MANOVA
    • ANOVA/MANOVA Introductory Overview - Contrast Analysis and Post-hoc Tests
    • Assumptions and Effects of Violating Assumptions - Deviation from Normal Distribution
      • Assumptions and Effects of Violating Assumptions - Homogeneity of Variances
      • Assumptions and Effects of Violating Assumptions - Homogeneity of Variances and Covariances
      • Assumptions and Effects of Violating Assumptions - Sphericity and Compound Symmetry
  • 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
  • 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
        • Analysis of Covariance (ANCOVA) - Basic Ideas
        • GLM Introductory Overview - One-Way ANOVA
        • GLM Introductory Overview - Main Effect ANOVA
        • GLM Introductory Overview - Factorial ANOVA
        • GLM Introductory Overview - Analysis of Covariance
        • 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
        • 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
  • 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
    • Zero Pivot Element Detected During Model Fitting
    • GLZ Introductory Overview - Interpretation of Results and Diagnostics
  • Generalized EM and k-Means Cluster Analysis Overview
  • 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
      • GLM 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
  • 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 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
  • Generalized Additive Models Overview
    • Generalized Additive Models - Program Overview
  • Goodness of Fit Calculations Overview
  • Independent Component Analysis Overview
  • Interactive Trees (C&RT, CHAID) Overview
    • Missing Data in GC&RT, GCHAID, and Interactive Trees
  • Log-Linear Analysis Overview
    • Two-way Frequency Tables
    • Multi-way Frequency Tables
    • The Log-Linear Model
    • Goodness-of-Fit
    • Automatic Model Fitting
  • Machine Learning Program Overview
    • Support Vector Machines Introductory Overview
  • 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
  • Multivariate Statistical Process Control (MSPC) and Nonlinear Iterative Partial Least Squares (NIPALS) Overview
  • 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
  • Multiple Regression Analysis Overview
    • Multiple Regression Analysis Introductory Overview - Computational Approach
      • 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
  • 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
  • Multivariate Adaptive Regression Splines (MARSplines) Overview
  • 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
      • Common Nonlinear Regression Models - Intrinsically Nonlinear 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
  • Nonparametric Methods Overview
    • Nonparametric Statistics Introductory Overview - When to Use Which Method
    • Nonparametric Correlations
  • Optimal Binning for Predictive Data Mining Overview
    • Optimal Binning for Predictive Data Mining Program Overview
  • 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
  • Predictor Screening Introductory Overview
  • Principal Components and Classification Analysis Overview
    • Principal Components & Classification Analysis - General Purpose
    • Principal Components and Classification Analysis
    • Principal Components and Classification Analysis - Computational Details
  • 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
  • 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
  • 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
  • Random Forests Overview
  • 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
  • Response Optimization Overview
  • 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
  • Sequence, Association and Link Analysis Module Overview
    • Sequence, Association, & Link Analysis (SAL) Technical Notes
  • Stepwise Model Builder Overview
    • Stepwise Model Builder - Logistic Regression
  • 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
  • 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
  • Text Mining and Document Retrieval Overview
  • 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
  • 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
  • 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
  • Weight of Evidence (WoE) Overview