MODEL Statement 
In the MODEL statement, you specify the response (dependent) variables followed by an equal sign and then the independent variables, some of which can be covariates.
Table 78.2 summarizes the options available in the MODEL statement. The statistic options specify which statistics are output to the OUT= data set. If none of the statistic options are selected, the data set is created but contains no observations. The statistic option keywords become values of the special variable _TYPE_ in the output data set.
Task 
Options 

Analyze original data 

Fit model to first BY group only 

Declare covariates 

Request additional statistics 

Request additional tests 

Suppress displayed output 

Task 
Statistic Options 
Output statistics 

The following list describes these options in alphabetical order.
specifies that the observed response values from the input data set be written to the output data set.
uses only the first BY group to estimate the model. Subsequent BY groups have scoring statistics computed in the output data set only. The BYOUT option is used only when a BY statement is specified.
declares that the first n variables on the right side of the model are simple linear regressors (covariates) and not factors in the quadratic response surface. By default, PROC RSREG forms quadratic and crossproduct effects for all regressor variables in the MODEL statement.
See the section Handling Covariates for more details and Example 78.2 for an example that uses covariates.
specifies that Cook’s influence statistic be written to the output data set.
See Chapter 4, Introduction to Regression Procedures, for details and formulas.
See Draper and Smith (1981) for a discussion of lackoffit tests.
specifies that the lower bound of a 95% confidence interval for an individual predicted value be written to the output data set. The variance used in calculating this bound is a function of both the mean square error and the variance of the parameter estimates.
See Chapter 4, Introduction to Regression Procedures, for details and formulas.
specifies that the lower bound of a 95% confidence interval for the expected value of the dependent variable be written to the output data set. The variance used in calculating this bound is a function of the variance of the parameter estimates.
See Chapter 4, Introduction to Regression Procedures, for details and formulas.
suppresses the display of the analysis of variance and parameter estimates from the model fit.
performs the canonical and ridge analyses with the parameter estimates derived from fitting the response to the original values of the factor variables, rather than their coded values (see the section Coding the Factor Variables for more details). Use this option if the data are already stored in a coded form.
suppresses the display of the canonical analysis for the quadratic response surface.
suppresses the display of both the analysis of variance and the canonical analysis.
specifies that the values predicted by the model be written to the output data set.
computes and displays the predicted residual sum of squares (PRESS) statistic for each dependent variable in the model. The PRESS statistic is added to the summary information at the beginning of the analysis of variance, so if the NOANOVA or NOPRINT option is specified, then the PRESS option has no effect.
See Chapter 4, Introduction to Regression Procedures, for details and formulas.
specifies that the residuals, calculated as ACTUAL PREDICTED, be written to the output data set.
specifies that the upper bound of a 95% confidence interval for an individual predicted value be written to the output data set. The variance used in calculating this bound is a function of both the mean square error and the variance of the parameter estimates.
See Chapter 4, Introduction to Regression Procedures, for details and formulas.
specifies that the upper bound of a 95% confidence interval for the expected value of the dependent variable be written to the output data set. The variance used in calculating this bound is a function of the variance of the parameter estimates.
See Chapter 4, Introduction to Regression Procedures, for details and formulas.