SURVEYLOGISTIC Procedure
The SURVEYLOGISTIC procedure fits linear logistic regression models for discrete response survey data by the method of
maximum likelihood. For statistical inferences, PROC SURVEYLOGISTIC incorporates complex survey sample designs, including
designs with stratification, clustering, and unequal weighting. The following are highlights of the SURVEYLOGISTIC procedure's features:
 fits models with binary, ordinal, or nominal dependent variables with the following link functions:
 logit
 probit
 complementary loglog
 generalized logit
 computes variances of the regression parameters and odds ratios by using the following methods:
 Taylor series (linearization)
 balanced repeated replication (BRR)
 delete1 jackknife
 enables you to employ Fay's method with BRR
 enables you to input or output a SAS data set containing a Hadamard matrix for BRR
 enables you to import or export SAS data sets containing replicate weights for BRR or jackknife methods
 creates a SAS data set that contains the jackknife coefficients
 provides analysis for subpopulations, or domains, in addition to analysis for the entire study population

 enables you to control the ordering of the response categories
 computes a generalized R2 measure for the fitted model
 tests linear hypotheses about the regression parameters
 enables you to specify units of change for continuous explanatory variables so that customized odds ratios can be estimated
 performs BY group processing, which enables you to obtain separate analyses on grouped observations (distinct from subpopulation analysis)
 creates a data set that contains the variables in the input data set, the estimated linear predictors and their standard error estimates, the estimates
of the cumulative or individual response probabilities, and the confidence limits for the cumulative probabilities
 creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details see the SURVEYLOGISTIC Procedure
Examples