[ REGRESS ] [ OPTIONS ] [ OUTPUT_GROUPS ] [ STATEMENTS ]
The REGRESS procedure fits linear models to sample survey data and other clustered data and repeated measures applications. Estimates of the model parameters and their standard errors are computed, along with tests of hypotheses. REGRESS offers GEE model fitting techniques for efficient parameter estimation. For estimating variance of the parameter estimates, REGRESS implements two robust methods described in Binder (1983) and Zeger and Liang (1986), as well as a model-based (naive) variance estimation method. A choice of independent or exchangeable "working" correlations is also provided. You can specify tests for linear combinations of the model parameters, and you can output the predicted values, residuals, parameter estimates, and their associated variance-covariance matrix for further hypothesis testing. Also, you can estimate and test linear combinations of the adjusted group means (also known as least squares means).