Statistics - Advanced Linear/Nonlinear Models - General Regression Models

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Advanced Models and on the menu, select General Regression to display the General Regression Models Startup Panel.

Classic menus. On the Statistics - Advanced Linear/Nonlinear Models submenu, select General Regression Models  to display the General Regression Models Startup Panel.

The General Regression Models (GRM) module is called a "general" regression models program because it applies the methods of the general linear model, allowing it to build models for designs with multiple-degrees-of-freedom effects for categorical predictor variables, as well as for designs with single-degree-of-freedom effects for continuous predictor variables. GRM implements stepwise and best-subset model-building techniques for univariate and multivariate analysis of variance (ANOVA/MANOVA), regression, and analysis of covariance (ANCOVA/MANCOVA) designs.

The General Regression Models (GRM) module offers all standard and unique results options described in the context of the GLM module, including desirability profiling, predicted and residual statistics for the computation or training sample, cross-validation or verification sample, and prediction sample; tests of assumptions, means plots, etc. In addition, unique regression-specific results options are also available, including Pareto charts of parameter estimates, whole model summaries (tests) with various methods for evaluating no-intercept models, partial and semi-partial correlations, etc.

To analyze simple or complex ANOVA/ANCOVA or MANOVA/MANCOVA designs, see also the General Linear Models (GLM) module; STATISTICA also includes an implementation of Generalized Linear/Nonlinear Models (GLZ) and Generalized Additive Models (GAM).