Modeling and Multivariate Methods - SAS

Chapter 3 Fitting Standard Least Squares Models 61
Regression Reports
Table 3.5 Description of the Parameter Estimates Report (Continued)
Std Beta Note: Only appears if you right-click in the report and select Columns >
Std Beta.
Shows parameter estimates that would have resulted from the regression if all
of the variables had been standardized to a mean of 0 and a variance of 1.
VIF Note: Only appears if you right-click in the report and select Columns >
VIF.
Shows the variance inflation factors. High VIFs indicate a collinearity
problem.
The VIF is defined as follows:
VIF
=
--------------
1
2
1 – R i
where R i 2 is the coefficient of multiple determination for the regression of x i
as a function of the other explanatory variables.
Design Std Error Note: Only appears if you right-click in the report and select Columns >
Design Std Error.
Shows standard error without being scaled by sigma (RMSE). Design Std
Error is equal to the following:
diag( X 'X) – 1
Note: For reports that do not appear by default, you can set them to always appear. Select File >
Preferences > Platforms > Fit Least Squares.
Effect Tests
The effect tests are joint tests in which all parameters for an individual effect are zero. If an effect has only
one parameter, as with simple regressors, then the tests are no different from the t-tests in the Parameter
Estimates report.
Note: Parameterization and handling of singularities are different from the SAS GLM procedure. For
details about parameterization and handling of singularities, see the “Statistical Details” on page 651
appendix.