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• SAS/STAT Procedures A-Z

The GLM Procedure

• fits general linear models including:
  • simple regression
  • multiple regression
  • analysis of variance for balanced or unbalanced data
  • analysis of covariance
  • response surface models
  • weighted regression
  • polynomial regression
  • multivariate analysis of variance (MANOVA)
  • partial correlation
  • repeated measures analysis of variance
• specify any degree of interaction (crossed effects) and nested effects
• specify polynomial, continuous-by-class, and continuous-nesting class effects
• absorb classification effects in a model
• allows you to specify random effects in a model
• produce expected mean squares for each Type I, Type II, Type III, Type IV, and contrast mean squares used in the analysis
• allows you to specify both hypothesis effects and the error effect to use for a multivariate analysis of variance
• obtain separate analyses on observation in groups
• compute least square means and least square mean differences for classification effects
• perform multiple comparison adjustments for the p-values and confidence limits for the least square mean differences
• compute arithmetic means and standard deviations of all continuous variables in a model within each group corresponding to each effect
• perform multiple comparison of main effect means
• test hypotheses for the effects of a linear model regardless of the number of missing cells or the extent of confounding
• perform F tests that use appropriate mean squares or linear combinations of mean squares as error terms
• estimate linear functions of the model parameters
• test hypotheses for linear combinations of the model parameters
• displays the sum of squares associated with each hypothesis tested and, upon request, the form of the estimable function employed in a test.
• produces the general form of all estimable functions
• create an output data set containing the input data set, predicted values, residuals, and other diagnostic measures
• use ODS to create a SAS data set corresponding to any table
• supports ODS Graphics

For further details see the SAS/STAT User's Guide: The GLM Procedure
( PDF | HTML )

Examples

• Randomized Complete Blocks with Means Comparisons and Contrasts
• Regression with Mileage Data
• Unbalanced ANOVA for Two-Way Design with Interaction
• Analysis of Covariance
• Three-Way Analysis of Variance with Contrasts
• Multivariate Analysis of Variance
• Repeated Measures Analysis of Variance
• Mixed Model Analysis of Variance with the RANDOM Statement
• Analyzing a Doubly Multivariate Repeated Measures Design
• Testing for Equal Group Variances
• Analysis of a Screening Design

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