Statistics Variable Lists

Basic Statistics and Tables

Descriptive Statistics

Variables: All dependent variables and predictors

Correlation Matrices

Variables: Continuous dependent variables and/or predictors

t-Test, Independent, by Groups

Variables: Continuous dependent variables and/or predictors

Grouping variables: Categorical dependent variables and/or predictors

t-Test for Independent Samples, by Variables

Variables: Continuous dependent variables and/or predictors

t-Test, Dependent Samples

Variables: Continuous dependent variables and/or predictors

t-Test, Single Sample

Variables: Continuous dependent variables and/or predictors

Frequency Tables

Variables: All dependent variables and predictors

Breakdown and One-Way ANOVA

Variables: Continuous dependent variables and/or predictors

Grouping variables: Categorical predictors

Breakdown non-factorial tables

Variables: Continuous dependent variables and/or predictors

Grouping variables: Categorical predictors

Crosstabulation Tables

Variables: Categorical dependent variables

Stub and Banner Tables

Variables: Categorical dependent variables and predictors

Testing Differences: r, Percent, Mean

Variables: No variables required

User-Defined 2 x 2 Tables

Variables: No variables required

Multiple Regression

Standard Multiple Regression

Dependent variables: Continuous dependent variables

Predictors: Continuous predictors

Stepwise Multiple Regression

Dependent variables: Continuous dependent variables

Predictors: Continuous predictors

ANOVA

Main Effects ANOVA

Dependent variables: Continuous dependent variables

Predictors: Categorical predictors

Factorial ANOVA

Dependent variables: Continuous dependent variables

Predictors: Categorical predictors

Repeated Measures ANOVA

Dependent variables: Continuous dependent variables

Predictors: Categorical predictors

Nonparametrics

Observed vs. Expected Chi-Square

Dependent variable: Continuous dependent variable

Predictor: Continuous predictor

Correlations (Spearman, Kendall Tau, Gamma)

Variables: Continuous dependent variables and/or predictors

Comparing Two Independent Samples (Groups)

Variables: Continuous dependent variables and/or predictors

Grouping variable: Categorical predictor

Comparing Multiple Indep. Samples (Groups)

Variables: Continuous dependent variables and/or predictors

Grouping variable: Categorical predictor

Comparing Two Dependent Samples (Variables)

Variables: Continuous dependent variables and/or predictors

Comparing Multiple Dep. Samples (Variables)

Variables: Continuous dependent variables and/or predictors

Distribution Fitting

Distribution Fitting

Variables: All dependent variables and predictors

Advanced Linear and Nonlinear Models

General Linear Models

Main Effects Linear Models

Dependent variables: Continuous dependent variables

Predictors: Categorical predictors

Factorial ANCOVA/MANCOVA Models

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Covariates: Continuous predictors

Repeated Measure Models

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Covariates: Continuous predictors

Response Surface and Mixture Models

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Covariates: Continuous predictors

General Linear Models

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Covariates: Continuous predictors

Generalized Linear and Nonlinear Models

Stepwise and Best Subset Probit Regression

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Count variable

Stepwise and Best Subset Logit Regression

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Count variable

Generalized Linear Models

Dependent variables: Categorical and/or continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

General Regression Models

Best-Subset and Stepwise Regression

Dependent variables: Continuous dependent variables

Regressors: Continuous predictors

Best-Subset and Stepwise ANCOVA

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

General Best-Subset and Stepwise Regression

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

General Partial Least Squares Models

PLS Regression Models

Dependent variables: Continuous dependent variables

Predictors: Continuous predictors

General PLS Models

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

NIPALS Algorithm (PCA, PLS)

NIPALS Algorithm Deployment

If you choose From PMML file, then any existing (user-defined) variable selections will be ignored.

Dependent Variables for Regression: Continuous dependent variables

Dependent Variables for Classification: Categorical dependent variables

Predictor Variables: Continuous or categorical predictor variables; requires minimum of two

NIPALS Algorithm for PCA

Variables: Categorical or Continuous variables

NIPALS Algorithm for PLS

Dependent Variables for Regression: Continuous dependent variables

Dependent Variables for Classification: Categorical dependent variables

Predictors: Continuous or Categorical variables; requires minimum of two

Variance Components

Variance Components

Variables: Continuous dependent variables

Factors: Categorical dependent variables and/or predictors

Covariates: Continuous predictors

Survival Analysis

Life Tables and Distributions

Survival variables: Continuous dependent variables

Censoring variable: Censoring variable

Kaplan-Meier Product-Limit Method

Survival variables: Continuous dependent variables

Censoring variable: Censoring variable

Comparing Survival in Two Groups

Survival variables: Continuous dependent variables

Group variable: Categorical predictor

Censoring variable: Censoring variable

Comparing Survival in Multiple Groups

Survival variables: Continuous dependent variables

Group variable: Categorical predictor

Censoring variable: Censoring variable

Regression Models

Survival variables: Continuous dependent variables

Independent variables: Continuous predictors

Group variable: Categorical predictor

Censoring variable: Censoring variable

Nonlinear Estimation

Quick Logit Regression

Dependent variables: Categorical dependent variables

Independent variables: Continuous predictors

Counts: Count variable

Quick Probit Regression

Dependent variables: Categorical dependent variables

Independent variables: Continuous predictors

Counts: Count variable

User Specified Regression, Least Squares

Dependent variable: Continuous dependent variable

Predictors: Continuous predictors

User Specified Regression and Loss Function

Dependent variable: Continuous dependent variable

Predictors: Continuous predictors

Log-Linear Analysis of Frequency Tables

Log-Linear Analysis

Count variables: Continuous dependent variables

Code variables: Categorical predictors

Time Series and Forecasting

Time Series Plots

Variables: Continuous dependent variables and/or continuous predictors

Single-Series Transformations (x=f(x))

Variables: Continuous dependent variables

Two-Series Transformation (x=f(x,y))

Variables: Continuous dependent variables

Second variable: Continuous predictor

Differencing, Time Series Transformations

Variables: Continuous dependent variables

Smoothing Transformations

Variables: Continuous dependent variables

Simple Fourier-Type Transformations

Variables: Continuous dependent variables and Continuous predictor

Autocorrelations and Crosscorrelations

Variables: Continuous dependent variables

Distributed Lags Analysis

Variables: Continuous dependent variables

Independent variable: Continuous predictor

Exponential Smoothing

Variables: Continuous dependent variables

ARIMA Models

Variables: Continuous dependent variables

Interrupted ARIMA

Variables: Continuous dependent variables

Seasonal Decomposition (Census I)

Variables: Continuous dependent variables

Single Series Spectral (Fourier) Analysis

Variables: Continuous dependent variables

Two Series Spectral (Fourier) Analysis

Variables: Continuous dependent variables

Dependent variable: Continuous predictor

X11/Y2K Census Method II Monthly

Variables: Continuous dependent variables

Start variables: Categorical predictors

X11/Y2K Census Method II Quarterly

Variables: Continuous dependent variables

Start variables: Categorical predictors

Structural Equation Modeling

Structural Equation Modeling

Variables: No variables required

Multivariate Exploratory Techniques

Cluster Analysis

K-Means Clustering

Variables: Continuous dependent variables

Tree Clustering (Joining)

Variables: Continuous dependent variables

Two-Way Joining Clustering

Variables: Continuous dependent variables

Factor Analysis

Factor Analysis

Variables: Continuous dependent variables

Principal Components and Classification Analysis

Principal Components and Classification Analysis

Variables for analysis: Continuous dependent variables

Supplementary variables: Continuous predictors

Variable with active cases: Learning/testing indicator

Grouping variable: Categorical dependent variable

Canonical Analysis

Canonical Analysis

Variables: Continuous dependent variables and/or Continuous predictor

Reliability and Item Analysis

Reliability and Item Analysis

Variables: Continuous dependent variables and/or Continuous predictor

Classification Trees

Classification from Ordered Predictors

Dependent variables: Categorical dependent variables

Ordered predictors: Continuous predictors

Sample identifier: Learning/testing variable

Classification from Categorical and Ordered Predictors

Dependent variables: Categorical dependent variables

Categorical predictors: Categorical predictors

Ordered predictors: Continuous predictors

Sample identifier: Learning/testing variable

Exhaustive (C and RT) Search for Univariate Splits

Dependent variables: Categorical dependent variables

Categorical predictors: Categorical predictors

Ordered predictors: Continuous predictors

Sample identifier: Learning/testing variable

Correspondence Analysis

Correspondence Analysis (CA)

Row and column variables: Categorical dependent variables and predictors

Variable with frequencies: Continuous dependent variable

Multiple Correspondence Analysis (MCA)

Row and column variables: Categorical dependent variables and predictors

Variable with frequencies: Continuous dependent variable

Multidimensional Scaling

Multidimensional Scaling

Variables: Continuous dependent variables

Discriminant Analysis

Standard Discriminant Analysis

Grouping variable: Categorical dependent variable

Independent variables: Continuous predictors

Stepwise Discriminant Analysis

Grouping variable: Categorical dependent variable

Independent variables: Continuous predictors

General Discriminant Analysis Models

Best-Subset and Stepwise GDA ANCOVA

Dependent variable: Categorical dependent variable

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

General Best-Subset and Stepwise Discriminant Analysis

Dependent variable: Categorical dependent variable

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Industrial Statistics and Six Sigma

Quality Control Charts

X and MR Chart for Variables, SixGraph Summary

Variables: Continuous dependent variables

X-Bar and R or S Chart for Variables, SixGraph summary

Variables: Continuous dependent variables

MA X-Bar and R or S Chart for Variables

Variables: Continuous dependent variables

EWMA X-Bar and R or S Chart for Variables

Variables: Continuous dependent variables

Attribute Charts (C, U, Np, p)

Variables: Continuous dependent variables

CuSum Chart for Observations

Variables: Continuous dependent variables

Multiple Stream Process (MSP) X-Bar and R or S Group Charts (GCC)

Variables: Continuous dependent variables

Multiple Stream Process (MSP) X and MR Chart for Variables

Variables: Continuous dependent variables

Multiple Stream Process (MSP) Attribute Charts (C, U, Np, p)

Variables: Continuous dependent variables

Pareto Chart

Variables: Continuous and/or categorical dependent variables

Hotelling T-Square Control Chart (HTS)

Variables: Continuous dependent variables

PLS, PCA, Multivariate/Batch SPC

Multivariate Statistical Process Control Deployment

If you choose From PMML file, then any existing (user-defined) variable selections will be ignored.

Dependent Variables for Regression: Continuous dependent variables

Dependent Variables for Classification: Categorical dependent variables

Predictor Variables: Continuous or categorical predictor variables; requires minimum of two

Count variable: Batch variable

Censoring indicator: Time variable

Principal Components Analysis

Variables: Categorical or Continuous variables; requires minimum of two

Partial Least Squares

Dependent Variables for Regression: Continuous dependent variables

Dependent Variables for Classification: Categorical dependent variables

Predictors: Continuous or Categorical variables; requires minimum of two

Batch-wise Multi-way Partial Least Squares

Count variable: Batch variable

Censoring indicator: Time variable, used as dependent variable

For non-aggregated data:

Process variables: Continuous or Categorical predictors; minimum of two

For aggregated data:

Process variable: Continuous predictor

Measurement variable: Categorical predictor

Time-wise Multi-way Principle Components Analysis

Count variable: Batch variable

Censoring indicator: Time variable

For non-aggregated data:

Process variables: Continuous or Categorical predictors; minimum of two

For aggregated data:

Process variable: Continuous predictor

Measurement variable: Categorical predictor

Time-wise Multi-way Partial Least Squares

Count variable: Batch variable

Censoring indicator: Time variable

Dependent variable: Continuous for regression, Categorical for classification

Predictor variables: Continuous or categorical; requires minimum of two

Process Analysis

Process Capability Analysis

Variables: Continuous dependent variables

Grouping variable: Categorical predictors

Generate Gage R and R Design

Variables: No variables required

Analyze Gage R and R Experiments

Measures: Continuous dependent variables

Coding variables: Categorical predictors

Gage Linearity

Measures: Continuous dependent variables

Master: Continuous predictor variable

Part: Categorical predictor variable

Sampling Plans

Variables: Continuous dependent variables

Weibull and Reliability/Failure Time Analysis

Variables: Continuous dependent variables

Censoring variable: Censoring variable

Weibull Probability Paper

Failure times: Continuous dependent variables

Censoring indicator: Censoring variable

Cause-Effect (Ishikawa, Fishbone) Diagrams

Variables: Categorical dependent variables and predictors

Experimental Design (DOE)

2**(K-p) Standard Design Creation

Variables: No variables required

2-Level Screening (Plackett-Burman) Design Creation

Variables: No variables required

2**(k-p) Max. Unconfounded and Min. Aberration Design Creation

Variables: No variables required

Mixed 2 and 3 Level Design Creation

Variables: No variables required

3**(K-p) and Box-Behnken Design Creation

Variables: No variables required

Central Composite Design Creation

Variables: No variables required

Latin Square Design Creation

Variables: No variables required

Taguchi Robust Design Creation

Variables: No variables required

Mixture Design Creation

Variables: No variables required

D- and A- (T-) Optimal Algorithmic Design Creation

Variables: No variables required

Analysis of 2**(K-p) and Screening Designs

Dependent variables: Continuous dependent variables

Factors and blocking variables: Categorical predictors

Analysis of Mixed 2 and 3 Level Designs

Dependent variables: Continuous dependent variables

Factors and blocking variables: Categorical predictors

Analysis of 3**(K-p) and Box-Behnken Designs

Dependent variables: Continuous dependent variables

Factors and blocking variables:  Categorical predictors

Analysis of Central Composite Designs

Dependent variables: Continuous dependent variables

Factors:  Continuous predictors

Blocking variables:  Categorical predictors

Analysis of Latin Square Designs

Dependent variables: Continuous dependent variables

Factors:  Categorical predictors

Analysis of Taguchi Robust Designs

Dependent variables: Continuous dependent variables

Factors:  Categorical predictors

Analysis of Mixture Designs

Dependent variables: Continuous dependent variables

Factors:  Continuous predictors

Variance Estimation and Precision

Variance Estimation and Precision

Variables: Continuous dependent variables

Factors: Categorical dependent variables and/or predictors

Covariates: Continuous predictors

Power Analysis

Power Analysis

No variable selections are required for Power Analysis nodes.

Text Mining

TextMiner

Variable containing document file names: Categorical predictor

Variable containing text (if Get Text From File = False): Categorical predictor

Data-Mining

Neural Networks

Multilayer Perceptron

Dependent variables: Continuous and/or categorical dependent variables

Predictors:  Continuous and/or categorical predictors

Learning variable: Learning/testing variable

Radial Basis Function (RBF)

Dependent variables: Continuous and/or categorical dependent variables

Predictors: Continuous and/or categorical predictors

Learning variable: Learning/testing variable

Probabilistic Neural Network

Dependent variables: Categorical dependent variables

Predictors: Continuous and/or categorical predictors

Learning variable: Learning/testing variable

Generalized Regression Neural Network

Dependent variables: Continuous and/or categorical dependent variables

Predictors: Continuous and/or categorical predictors

Learning variable: Learning/testing variable

Linear

Dependent variables: Continuous and/or categorical dependent variables

Predictors: Continuous and/or categorical predictors

Learning variable: Learning/testing variable

Principal components network

Predictors: Continuous and/or categorical predictors

Learning variable: Learning/testing variable

Clustering Network

Dependent variables: Categorical dependent variables

Predictors: Continuous and/or categorical predictors

Learning variable: Learning/testing variable

Independent Component Analysis

Independent Component Analysis

Variables for analysis: Continuous predictor variables

Independent Component Analysis Deployment

If you choose From PMML file, then any existing (user-defined) variable selections will be ignored.

Variables for analysis: Continuous predictor variables

Generalized Cluster Analysis

Generalized K-means Cluster Analysis

Variables: Continuous or Categorical dependent variables

Generalized EM Cluster Analysis

Variables: Continuous or Categorical dependent variables

General Classification and Regression Tree Models

Standard Classification Trees (C And RT)

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

General Classification Trees (C And RT)

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Standard Regression Trees (C And RT)

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

General Regression Trees (C And RT)

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

General CHAID Models

Standard Classification CHAID

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Exhaustive Classification CHAID

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

General Classification CHAID

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Standard Regression CHAID

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Exhaustive Regression CHAID

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

General Regression CHAID

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Advanced C and RT, CHAID (Using Interactive Trees)

Advanced Classification Trees (C and RT)

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Advanced Regression Trees (C and RT)

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Advanced Classification CHAID

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Advanced Regression CHAID

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Boosted Trees

Boosting Classification Trees

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Boosting Regression Trees

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Random Forest

Random Forest Classification

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Random Forest Regression

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Continuous predictors: Continuous predictors

Count variable: Counts variable

Sample identifier variable: Learning/testing variable

Generalized Additive Models

GAM: Normal, Gamma, Poisson

Dependent variables: Continuous dependent variables

Categorical factors: Categorical predictors

Covariates: Continuous predictors

Generalized Additive Logit Models

Dependent variables: Categorical dependent variables

Categorical factors: Categorical predictors

Covariates: Continuous predictors

MARSplines

MARSplines

Dependent variables: Continuous or categorical variables

Predictors: Continuous or categorical predictors

Machine Learning

Support Vector Machines

Dependent variables: Continuous or categorical variables

Predictors: Continuous or categorical predictors

Sample identifier variable: Learning/testing variable

Naive Bayes

Dependent variables: Categorical variables

Predictors: Continuous or categorical predictors

Sample identifier variable: Learning/testing variable

K-Nearest Neighbors

Dependent variables: Continuous or categorical variables

Predictors: Continuous or categorical predictors

Sample identifier variable: Learning/testing variable

Rapid Deployment

Rapid Deployment

If you choose From PMML file, then any existing (user-defined) variable selections will be ignored.

Outcome variable: Continuous or categorical dependent variable

Predictor variables: Continuous or categorical predictors

Goodness of Fit

Goodness of fit

Observed variable: Continuous or categorical dependent variable

Predicted variables:  Continuous or categorical predictors

Goodness of fit for multiple inputs

Observed variable: Continuous or categorical dependent variable

Predicted variables: Continuous or categorical predictors

Feature Selection

Feature Select and Root Cause Analysis

Dependent variables: Continuous and/or categorical dependent variable

Predictor variables: Continuous and/or categorical predictors

Counts variable: Count variable

Combining Groups

Combining Groups

Outcome variable: Continuous variable (for regression) or Categorical variable (for classification)

Class predictor: Categorical predictor

Target variables: Do not need to select. New codes will be placed in the class predictor unless Add new variables is set to True.

Combining Groups Regression with Deployment

If you choose From PMML file, then any existing (user-defined) variable selections will be ignored.

Outcome variable: Continuous dependent variable

Class predictor: Categorical predictor

Target variables: Do not need to select. New codes will be placed in the class predictor unless Add new variables is set to True.

Combining Groups Classification with Deployment

If you choose From PMML file, then any existing (user-defined) variable selections will be ignored.

Outcome variable: Categorical dependent variable

Class predictor: Categorical predictor

Target variables: Do not need to select. New codes will be placed in the class predictor unless Add new variables is set to True.