Introduction to Analysis of Variance Procedures

Procedures That Perform Sum of Squares Analysis of Variance

The flagship procedure in SAS/STAT software for linear modeling with sum of squares analysis techniques is the GLM procedure. It handles most standard analysis of variance problems. The following list provides descriptions of PROC GLM and other procedures that are used for more specialized situations:


performs analysis of variance, multivariate analysis of variance, and repeated measures analysis of variance for balanced designs. PROC ANOVA also performs multiple comparison tests on arithmetic means.


performs analysis of variance, regression, analysis of covariance, repeated measures analysis, and multivariate analysis of variance. PROC GLM produces several diagnostic measures, performs tests for random effects, provides contrasts and estimates for customized hypothesis tests, provides tests for means adjusted for covariates, and performs multiple-comparison tests on both arithmetic and adjusted means.


computes the analysis of variance and analysis of simple covariance for data from an experiment with a lattice design. PROC LATTICE analyzes balanced square lattices, partially balanced square lattices, and some rectangular lattices.


performs mixed model analysis of variance and repeated measures analysis of variance via covariance structure modeling. When you choose one of the method-of-moment estimation techniques, the MIXED procedure produces an analysis of variance table with sums of squares, mean squares, and expected mean squares. PROC MIXED constructs statistical tests and intervals, enables customized contrasts and estimates, and computes empirical Bayes predictions.


performs analysis of variance and analysis of covariance for purely nested random models.


performs regression by using the Gentleman-Givens computational method. For ill-conditioned data, PROC ORTHOREG can produce more accurate parameter estimates than other procedures, such as PROC GLM. See ChapterĀ 84: The ORTHOREG Procedure, for more information.


estimates variance components for random or mixed models. If you choose the METHOD=TYPE1 or METHOD=GRR option, the VARCOMP procedure produces an analysis of variance table with sums of squares that correspond to the random effects in your models.


fits univariate and multivariate linear models, optionally with spline and other nonlinear transformations. Models include ordinary regression and ANOVA, multiple and multivariate regression, metric and nonmetric conjoint analysis, metric and nonmetric vector and ideal point preference mapping, redundancy analysis, canonical correlation, and response surface regression. See ChapterĀ 117: The TRANSREG Procedure, for more information.