General Regression Models (GRM) - Introductory Overview

The General Regression Models (GRM) module is called a "general" regression 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 Analysis of Variance (ANOVA), regression, and analysis of covariance (ANCOVA) designs. GRM uses the least squares methods of the general linear model to build models and to estimate and test hypotheses about effects included in the final model.

The General Linear Models (GLM) module can analyze designs with any number and type of effects. STATISTICA GRM offers most of the analysis options of GLM, but in addition provides model-building methods for finding the "best" model from a number of possible models. GRM is a "sister program" to GLM in the sense that there is considerable overlap between the programs, but also important unique functionality in each program. For related methods, see also Methods for Analysis of Variance.

The Introductory Overview topics listed below describe the use of the general linear model for finding the "best" linear model from a number of possible models. If you are unfamiliar with the basic methods of ANOVA and regression in linear models, it may be useful to first review the basic information on these topics in Elementary Concepts. A detailed discussion of univariate and multivariate ANOVA techniques can also be found in the Introductory Overview of the ANOVA/MANOVA module; a discussion of Multiple Regression methods is provided in the Overviews. Discussion of the ways in which the linear regression model is extended by the general linear model can be found in the Introductory Overview of the GLM module.

GRM Introductory Overview Topics

Basic Ideas: The Need for Simple Models

See also GRM - Index, or Methods for Analysis of Variance.