Model Fitting: Linear Regression: Output Variables Tab

Output Variables Tab

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. $\text{[math]}$ represents the name of the response variable.

Predicted values: adds predicted values. The variable is named RegP_ $\text{[math]}$ .
Confidence limits for means: adds 95% confidence limits for the expected value (mean). The variables are named RegLclm_ $\text{[math]}$ and RegUclm_ $\text{[math]}$ .
Prediction limits for individuals: adds 95% confidence limits for an individual prediction. The variables are named RegLcli_ $\text{[math]}$ and RegUcli_ $\text{[math]}$ .
Raw residuals: adds residuals, which are calculated as observed values minus predicted values. The variable is named RegR_ $\text{[math]}$ .
Internally studentized residuals: 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_ $\text{[math]}$ .
Externally studentized residuals: 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_ $\text{[math]}$ .
Cook’s D: adds Cook’s $\text{[math]}$ influence statistic. The variable is named RegCooksD_ $\text{[math]}$ .
Leverage (H): adds the leverage statistic. The variable is named RegH_ $\text{[math]}$ .
PRESS residuals: adds the PRESS residuals. This is the $\text{[math]}$ th residual divided by $\text{[math]}$ , where $\text{[math]}$ is the leverage and where the model has been refit without the $\text{[math]}$ th observation. The variable is named RegPRESS_ $\text{[math]}$ .
COVRATIO (influence on covariance of coefficients): adds the covariance ratio. This is the $\text{[math]}$ th residual divided by $\text{[math]}$ , where $\text{[math]}$ is the leverage and where the model has been refit without the $\text{[math]}$ th observation. The variable is named RegCovRatio_ $\text{[math]}$ .
DFFITS (influence on predicted values): adds the standard influence of observation on the predicted value. The variable is named RegDFFITS_ $\text{[math]}$ .
DFBETAS (influence on coefficients): adds $\text{[math]}$ variables, where $\text{[math]}$ 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 $\text{[math]}$ th observation. Large values of DFBETAS indicate observations that are influential in estimating a given parameter. Belsley, Kuh, and Welsch (1980) recommend $\text{[math]}$ as a size-adjusted cutoff. The variables are named DFB_ $\text{[math]}$ , where $\text{[math]}$ is the name of the $\text{[math]}$ th regressor (including the intercept).

Figure 21.17 The Output Variables Tab