GLM - Introductory Overview

The GLM module provides a comprehensive set of techniques for analyzing any univariate or multivariate Analysis of Variance (ANOVA), regression, or Analysis of Covariance (ANCOVA) design. GLM uses the least square methods of the general linear model to estimate and test hypotheses about effects. There are several modules in STATISTICA that will perform ANOVA for factorial or specialized designs. For a discussion of these modules and the types of designs for which they are best suited refer to Methods for Analysis of Variance. The GLM module can analyze designs with any number and type of effects. Note that STATISTICA also includes General Regression Models (GRM), which offers most of the options of GLM, and in addition includes various model building techniques, including forward and backward stepwise regression and best subset regression.

The Introductory Overview topics listed below describe the use of the general linear model in a wide variety of statistical analyses. 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 Introductory Overview of the ANOVA/MANOVA module.

Basic Ideas: Analysis of Variance and Covariance (ANOVA/MANOVA)

See also Analyzing Designs with Random Effects Using GLM vs. Variance Estimation and Precision and GLM - Index.