Overview: GLM Procedure

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 can 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 the following:

• 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