Logistic Regression (Logit) and/or Probit Regression

Select Quick Logit regression from the Nonlinear Estimation Startup Panel - Quick tab to display the Logistic Regression (Logit) dialog box, which contains one tab: Quick.

Note that the options in this dialog box are identical to the options on the Probit Regression dialog box, which is displayed when you select Quick Probit regression from the Nonlinear Estimation Startup Panel - Quick tab. The Probit Regression dialog box contains one tab: Quick.

OK. Click the OK button to display either the standard variable selection dialog box or the Model Estimation dialog box (if variables were already selected via the Quick tab).

Cancel. Click the Cancel button to return to the Nonlinear Estimation Startup Panel.

Options. Click the Options button to display the Options menu.

Select Cases. Click the Select Cases button to display the Analysis/Graph Case Selection Conditions dialog box, which is used to create conditions for which cases will be included (or excluded) in the current analysis. More information is available in the case selection conditions overview, syntax summary, and dialog description.

W. Click the W (Weight) button to display the Analysis/Graph Case Weights dialog box, which is used to adjust the contribution of individual cases to the outcome of the current analysis by "weighting" those cases in proportion to the values of a selected variable.

MD deletion. Missing data can be deleted Casewise or by Mean Substitution depending on the selection in this group box.

Casewise. If this option button is selected, only cases that do not contain any missing data for any of the variables selected for the analysis will be included in the analysis.

Mean substitution. If this option button is selected, missing data will be replaced by the means for the respective variables (for this analysis only, not in the data file).

Note: Generalized Linear/Nonlinear Model (GLZ). You can also use the Generalized Linear/Nonlinear Model (GLZ) module to analyze continuous, binomial, or multinomial dependent variables. GLZ is an implementation of the generalized linear model and allows you to compute a standard, stepwise, or best subset multiple regression analysis with continuous as well as categorical predictors, and for continuous,  binomial, or multinomial dependent variables (probit regression, binomial and multinomial logit regression, Poisson regression, etc.; see also Link functions). In general, the estimation algorithms implemented in the Generalized Linear/Nonlinear Models (GLZ) module are more efficient, and STATISTICA only includes these models here for compatibility purposes.