You can use the Output Variables tab to add analysis variables to the data table. (See Figure 21.17.) If you request a plot that uses one of the output variables, then that variable is automatically created even if you did not explicitly select the variable on the Output Variables tab.
The following list describes each output variable and indicates how the output variable is named. Y represents the name of the response variable.
adds predicted values. The variable is named RegP_
Y.
adds 95% confidence limits for the expected value (mean). The variables are named RegLclm_
Y and RegUclm_
Y.
adds 95% confidence limits for an individual prediction. The variables are named RegLcli_
Y and RegUcli_
Y.
adds residuals, which are calculated as observed values minus predicted values. The variable is named RegR_
Y.
adds internally studentized residuals, which are the residuals divided by their standard errors. (These correspond to the
STUDENT= option in the OUTPUT statement.) The variable is named RegIntR_
Y.
adds externally studentized residuals, which are studentized residuals with the current observation deleted. (These correspond
to the RSTUDENT= option in the OUTPUT statement.) The variable is named RegExtR_
Y.
adds Cook’s D influence statistic. The variable is named RegCooksD_
Y.
adds the leverage statistic. The variable is named RegH_
Y.
adds the PRESS residuals. This is the ith residual divided by , where h is the leverage and where the model has been refit without the ith observation. The variable is named RegPRESS_
Y.
adds the covariance ratio. This is the ith residual divided by , where h is the leverage and where the model has been refit without the ith observation. The variable is named RegCovRatio_
Y.
adds the standard influence of observation on the predicted value. The variable is named RegDFFITS_
Y.
adds p variables, where p is the number of parameters in the model. The variables are scaled measures of the change in each parameter estimate and
are calculated by deleting the ith observation. Large values of DFBETAS indicate observations that are influential in estimating a given parameter. Belsley,
Kuh, and Welsch (1980) recommend as a size-adjusted cutoff. The variables are named DFB_
, where is the name of the jth regressor (including the intercept).