The GLM Procedure

Overview

The GLM procedure uses the method of least squares to fit general linear models. Among the statistical methods available in PROC GLM are regression, analysis of variance, analysis of covariance, multivariate analysis of variance, and partial correlation.

PROC GLM analyzes data within the framework of General linear models. PROC GLM handles models relating one or several continuous dependent variables to one or several independent variables. The independent variables may be either classification variables, which divide the observations into discrete groups, or continuous variables. Thus, the GLM procedure can be used for many different analyses, including

simple regression
multiple regression
analysis of variance (ANOVA), especially for unbalanced data
analysis of covariance
response-surface models
weighted regression
polynomial regression
partial correlation
multivariate analysis of variance (MANOVA)
repeated measures analysis of variance

PROC GLM Features

PROC GLM Contrasted with Other SAS Procedures

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