<label:> MODEL
dependents = <regressors> </ options> ;
After the keyword MODEL, the dependent (response) variables are specified, followed by an equal sign and the regressor variables.
Variables specified in the MODEL statement must be numeric variables in the data set being analyzed. For example, if you want to specify a quadratic term
for variable X1
in the model, you cannot use X1*X1 in the MODEL statement but must create a new variable (for example, X1SQUARE=X1*X1) in a DATA step and use this new variable in the MODEL statement. The label in the MODEL statement is optional.
Table 83.5 summarizes the options available in the MODEL statement. Equations for the statistics available are given in the section Model Fit and Diagnostic Statistics.
Table 83.5: MODEL Statement Options
Option 
Description 

Model Selection and Details of Selection 

Specifies model selection method 

Specifies maximum number of subset models displayed or output to the OUTEST= data set 

Produces summary statistics at each step 

Specifies the display details for FORWARD, BACKWARD, and STEPWISE methods 

Provides names for groups of variables 

Includes first n variables in the model 

Specifies maximum number of steps that might be performed 

Fits a model without the intercept term 

Performs incomplete principal component analysis and outputs estimates to the OUTEST= data set 

Performs ridge regression analysis and outputs estimates to the OUTEST= data set 

Sets criterion for entry into model 

Sets criterion for staying in model 

Specifies number of variables in model to begin the comparing and switching process 

Stops selection criterion 

Statistics 

Computes adjusted R square 

Computes Akaike’s information criterion 

Computes parameter estimates for each model 

Computes Sawa’s Bayesian information criterion 

Computes Mallows’ statistic 

Computes estimated MSE of prediction assuming multivariate normality 

Computes , the final prediction error 

Computes MSE for each model 

Computes Amemiya’s prediction criterion 

Displays root MSE for each model 

Computes the SBC statistic 

Computes statistic for each model 

Computes error sum of squares for each model 

Data Set Options 

Outputs the number of regressors, the error degrees of freedom, and the model R square to the OUTEST= data set 

Outputs standard errors of the parameter estimates to the OUTEST= data set 

Outputs standardized parameter estimates to the OUTEST= data set. Use only with the RIDGE= or PCOMIT= option. 

Outputs the variance inflation factors to the OUTEST= data set. Use only with the RIDGE= or PCOMIT= option. 

Outputs the PRESS statistic to the OUTEST= data set 

Has same effect as the EDF option 

Regression Calculations 

Displays inverse of sums of squares and crossproducts 

Displays sumsofsquares and crossproducts matrix 

Details on Estimates 

Displays heteroscedasticity consistent covariance matrix of estimates and heteroscedasticityconsistent standard errors 

Specifies method for computing the asymptotic heteroscedasticityconsistent covariance matrix 

Produces collinearity analysis 

Produces collinearity analysis with intercept adjusted out 

Displays correlation matrix of estimates 

Displays covariance matrix of estimates 

Displays heteroscedasticityconsistent standard errors 

Specifies method for computing the asymptotic heteroscedasticityconsistent covariance matrix 

Performs lackoffit test 

Displays squared semipartial correlation coefficients computed using Type I sums of squares 

Displays squared partial correlation coefficients computed using Type I sums of squares 

Displays squared partial correlation coefficients computed using Type II sums of squares 

Displays squared semipartial correlation coefficients computed using Type I sums of squares 

Displays squared semipartial correlation coefficients computed using Type II sums of squares 

Displays a sequence of parameter estimates during selection process 

Tests that first and second moments of model are correctly specified 

Displays the sequential sums of squares 

Displays the partial sums of squares 

Displays standardized parameter estimates 

Displays tolerance values for parameter estimates 

Displays heteroscedasticityconsistent standard errors 

Computes varianceinflation factors 

Predicted and Residual Values 

Computes % confidence limits for the parameter estimates 

Computes % confidence limits for an individual predicted value 

Computes % confidence limits for the expected value of the dependent variable 

Computes a DurbinWatson statistic 

Computes a DurbinWatson statistic and pvalue 

Computes influence statistics 

Computes predicted values 

Displays partial regression plots for each regressor 

Displays partial regression data 

Produces analysis of residuals 

Display Options and Other Options 

Requests the following options: 

Sets significance value for confidence and prediction intervals and tests 

Suppresses display of results 

Specifies the true standard deviation of error term for computing CP and BIC 

Sets criterion for checking for singularity 
You can specify the following options in the MODEL statement after a slash (/).