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 PMMLfile, 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 PMMLfile, 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 PMMLfile, 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 PMMLfile, 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.