You can use the Output Variables tab to add analysis variables to the data table. (See Figure 22.5.) 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. 
represents the name of the response variable.
- Predicted values
-
adds predicted values. The variable is named
RobP_.
- Final weights (M and MM methods only)
-
adds the final weights used in the iteratively reweighted least squares algorithm. The variable is named
RobWt_.
- Robust residuals
-
adds residuals, which are calculated as observed values minus predicted values. The variable is named
RobR_.
- Internally studentized robust residuals
-
adds internally studentized residuals, which are the residuals divided by their standard errors. The variable is named
RobIntR_.
- Robust MCD distance
-
adds a robust measure of distance between an observation and a robust estimate of location. The variable is named
RobRD_.
- Mahalanobis distance
-
adds the Mahalanobis distance between an observation and the multivariate mean of the data. The variable is named
RobMD_.
- Outlier indicator
-
adds an indicator variable for outliers. The variable is named
RobOut_.
- Leverage indicator
-
adds an indicator variable for leverage points. The variable is named
RobLev_.