The GLM Procedure
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Overview
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Getting Started
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Syntax
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Details
Statistical Assumptions for Using PROC GLM Specification of Effects Using PROC GLM Interactively Parameterization of PROC GLM Models Hypothesis Testing in PROC GLM Effect Size Measures for F Tests in GLM Absorption Specification of ESTIMATE Expressions Comparing Groups Multivariate Analysis of Variance Repeated Measures Analysis of Variance Random-Effects Analysis Missing Values Computational Resources Computational Method Output Data Sets Displayed Output ODS Table Names ODS Graphics
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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
- References
| TEST Statement |
- TEST <H=effects> E=effect </ options> ;
By default, for each sum of squares in the analysis an 
value is computed that uses the residual MS as an error term. Use a TEST statement to request additional tests that use other effects as error terms. You need a TEST statement when a nonstandard error structure (as in a split-plot design) exists. Note, however, that this might not be appropriate if the design is unbalanced, since in most unbalanced designs with nonstandard error structures, mean squares are not necessarily independent with equal expectations under the null hypothesis.
Caution: The GLM procedure does not check any of the assumptions underlying the statistic. When you specify a TEST statement, you assume sole responsibility for the validity of the 
statistic produced. To help validate a test, you can use the RANDOM statement and inspect the expected mean squares, or you can use the TEST option of the RANDOM statement.
You can use as many TEST statements as you want, provided that they appear after the MODEL statement.
You can specify the following terms in the TEST statement.
- H=effects
specifies which effects in the preceding model are to be used as hypothesis (numerator) effects.
- E=effect
specifies one, and only one, effect to use as the error (denominator) term. The E= specification is required.
By default, the sum of squares type for all hypothesis sum of squares and error sum of squares is the highest type computed in the model. If the hypothesis type or error type is to be another type that was computed in the model, you should specify one or both of the following options after a slash (/).
- ETYPE=n
specifies the type of sum of squares to use for the error term. The type must be a type computed in the model (
=1, 2, 3, or 4 ). - HTYPE=n
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specifies the type of sum of squares to use for the hypothesis. The type must be a type computed in the model (
=1, 2, 3, or 4). This example illustrates the TEST statement with a split-plot model:
proc glm; class a b c; model y=a b(a) c a*c b*c(a); test h=a e=b(a)/ htype=1 etype=1; test h=c a*c e=b*c(a) / htype=1 etype=1; run;