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
| WEIGHT Statement |
When a WEIGHT statement is used, a weighted residual sum of squares
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is minimized, where 
is the value of the variable specified in the WEIGHT statement, 
is the observed value of the response variable, and 
is the predicted value of the response variable.
If you specify the WEIGHT statement, it must appear before the first RUN statement or it is ignored.
An observation is used in the analysis only if the value of the WEIGHT statement variable is nonmissing and greater than zero.
The WEIGHT statement has no effect on degrees of freedom or number of observations, but it is used by the MEANS statement when calculating means and performing multiple comparison tests (as described in the section MEANS Statement).
The normal equations used when a WEIGHT statement is present are
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where 
is a diagonal matrix consisting of the values of the variable specified in the WEIGHT statement.
If the weights for the observations are proportional to the reciprocals of the error variances, then the weighted least squares estimates are best linear unbiased estimators (BLUE).