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The MIXED procedure fits a variety of mixed linear models to data and enables you to use these fitted models to make statistical inferences about the data.
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The LOGISTIC procedure fits linear logistic regression models for discrete response data by the method of maximum likelihood. It can also perform conditional logistic regression for binary response data and exact conditional logistic regression for binary and nominal response data.
The GENMOD procedure fits generalized linear models.
The FREQ procedure produces one-way to n-way frequency and contingency (crosstabulation) tables. For two-way tables, PROC FREQ computes tests and measures of association. For n-way tables, PROC FREQ provides stratified analysis by computing statistics across, as well as within, strata.
The PHREG procedure performs regression analysis of survival data based on the Cox proportional hazards model and its extensions.
The REG procedure is a general-purpose procedure for linear regression.
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.