SAS/STAT Software

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. The following are highlights of the procedure's features:

  • enables you to specify any degree of interaction (crossed effects) and nested effects
  • enables you to specify polynomial, continuous-by-class, and continuous-nesting class effects
  • enables you to absorb classification effects in a model
  • enables you to specify random effects in a model
  • produces expected mean squares for each Type I, Type II, Type III, Type IV, and contrast mean squares used in the analysis
  • enables you to specify both hypothesis effects and the error effect to use for a multivariate analysis of variance
  • performs BY group processing, which enables you to obtain separate analyses on grouped observations
  • computes least square means and least square mean differences for classification effects
  • performs multiple comparison adjustments for the p-values and confidence limits for the least square mean differences
  • computes arithmetic means and standard deviations of all continuous variables in a model within each group corresponding to each effect
  • performs multiple comparison of main effect means
  • tests hypotheses for the effects of a linear model regardless of the number of missing cells or the extent of confounding
  • performs F tests that use appropriate mean squares or linear combinations of mean squares as error terms
  • estimates linear functions of the model parameters
  • tests 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
  • creates an output data set that contains the input data set, predicted values, residuals, and other diagnostic measures
  • creates a SAS data set that corresponds to any output table
  • automatically creates graphs by using ODS Graphics

For further details see the GLM Procedure