Binary logistic regression: Save to dataset
The Save to dataset dialog provides options for saving values predicted by the model, residuals, and influence statistics as new variables in the Data Editor. Many of these variables can be used for examining assumptions about the data. To save the values for use in another IBM® SPSS® Statistics session, you must save the current data file.
- Predicted values
- Saves values predicted by the model and adds the selected items as new variables to the active dataset.
- Probabilities
- For each case, saves the predicted probability of occurrence of the event. A table in the output displays name and contents of any new variables. The "event" is the category of the dependent variable with the higher value; for example, if the dependent variable takes values 0 and 1, the predicted probability of category 1 is saved.
- Group membership
- The group with the largest posterior probability, based on discriminant scores. The group the model predicts the case belongs to.
- Residuals
- Produces a casewise listing of the temporary values that are created by
logistic regression and adds the selected items as new variables to the active dataset.
- Unstandardized
- The difference between an observed value and the value predicted by the model.
- Logit
- The residual for the case if it is predicted in the logit scale. The logit residual is the residual divided by the predicted probability times 1 minus the predicted probability.
- Studentized
- The change in the model deviance if a case is excluded.
- Standardized
- The residual divided by an estimate of its standard deviation. Standardized residuals, which are also known as Pearson residuals, have a mean of 0 and a standard deviation of 1.
- Deviance
- Residuals based on the model deviance.
- Influence statistics
- Saves values from statistics that measure the influence of cases on predicted values, and adds
the selected items as new variables to the active dataset.
- Cook's distances
- The logistic regression analog of Cook's influence statistic. A measure of how much the residuals of all cases would change if a particular case were excluded from the calculation of the regression coefficients.
- Leverage values
- The relative influence of each observation on the model's fit.
- DfBetas
- The difference in beta value is the change in the regression coefficient that results from the exclusion of a particular case. A value is computed for each term in the model, including the constant.
Saving new variables for Binary logistic regression
This feature requires Custom Tables and Advanced Statistics.
- From the menus choose:
- In the Binary logistic regression dialog, expand the Additional settings menu and click Save to dataset.