[ 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).
Procedure Enhancements:
Analysis of multiply imputed data has been implemented to produce all estimates, variances and tests of hypothesis.
The CLASS statement is available in REGRESS.
Estimates of confidence limits for the model parameters are now produced by default in the BETAS group.
A new continuous_variable=(value(s)) option on the CONDMARG, PREDMARG, COND_EFF And PRED_EFF statements optionally specifies particular values at which the predicted and conditional marginals and their contrasts are to be evaluated with respect to that continuous variable.