GRM Introductory Overview - Comparison with Other Regression Programs
STATISTICA General Regression Models provides a unique, highly flexible implementation of the general linear model. Specifically, GRM's implementation permits you to build models for highly complex designs, including designs with effects for categorical predictor variables. Thus, the "general" in General Regression Models refers both to the use of the general linear model, and to the fact that unlike most other regression programs, GRM is not limited to the analysis of designs that contain only continuous predictor variables.
GRM is a "sister program" to the even more comprehensive STATISTICA General Linear Model module. Both modules provide similar methods for specifying analyses and producing results, so learning how to use one module makes it very easy to use the other. With the exception of options for using the overparameterized model and options for analyzing incomplete designs, all the innovative features of GLM are also available in GRM. The following topics highlight only some of the unique features of the GLM and GRM modules, that are usually not found in other (less complete) implementations of the general linear model.
Some unique features and facilities of GLM and GRM