Logistic Regression Save New Variables

You can save results of the logistic regression as new variables in the active dataset:

Predicted Values. Saves values predicted by the model. Available options are Probabilities and Group membership.

  • 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.
  • Predicted Group Membership. The group with the largest posterior probability, based on discriminant scores. The group the model predicts the case belongs to.

Influence. Saves values from statistics that measure the influence of cases on predicted values. Available options are Cook's, Leverage values, and DfBeta(s).

  • Cook's. 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 Value. 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.

Residuals. Saves residuals. Available options are Unstandardized, Logit, Studentized, Standardized, and Deviance.

  • Unstandardized Residuals. The difference between an observed value and the value predicted by the model.
  • Logit Residual. 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 Residual. The change in the model deviance if a case is excluded.
  • Standardized Residuals. 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.

Export model information to XML file. Parameter estimates and (optionally) their covariances are exported to the specified file in XML (PMML) format. You can use this model file to apply the model information to other data files for scoring purposes. See the topic Scoring Wizard for more information.

Saving New Variables

This feature requires Custom Tables and Advanced Statistics.

  1. From the menus choose:

    Analyze > Regression > Binary Logistic…

  2. In the Logistic Regression dialog box, click Save.