You can use the Output Variables tab to add analysis variables to the data table. (See Figure 23.13.) 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. If you use events/trials syntax, then represents the name of the events variable.
- Proportions for events/trials
-
adds a variable named
Proportion_, where
is the name if the events variable and 
is the name of the trials variable. The value of the variable is the ratio 
. This variable is added only when you use events/trials syntax.
- Predicted probabilities
-
adds predicted probabilities. The variable is named
LogiP_.
- Confidence limits for predicted probabilities
-
adds 95% confidence limits for the predicted probabilities. The variables are named
LogiLclm_and
LogiUclm_.
- Linear predictor (log odds)
-
adds the linear predictor values. The variable is named
LogiXBeta_.
- Pearson chi-square residuals
-
adds the Pearson chi-square residuals. The variable is named
LogiChiSqR_.
- Deviance residuals
-
adds the deviance residuals. The variable is named
LogiDevR_.
- Confidence interval displacement (C)
-
adds the confidence interval displacement diagnostic,
. The variable is named LogiC_.
- Scaled confidence interval displacement (CBAR)
-
adds the confidence interval displacement diagnostic,
. The variable is named LogiCBar_.
- Leverage (H)
-
adds the leverage statistic. The variable is named
LogiH_.
- DIFCHISQ (influence on chi-square goodness-of-fit)
-
adds the change in the chi-square goodness-of-fit statistic that is attributed to deleting the individual observation. The variable is named
LogiDifChiSq_.
- DIFDEV (influence on deviance)
-
adds the change in the deviance that is attributed to deleting the individual observation. The variable is named
LogiDifDev_.
- DFBETAS (influence on coefficients)
-
adds
variables, where 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 
th observation. Large probabilities of DFBETAS indicate observations that are influential in estimating a given parameter.
The variables are named DFBETA_, where
is the name of an interval regressor (including the intercept). For classification variables, the variables are named DFBETA_, where
is the name of the variable and 
represents a level.
