Logistic Regression (Logit) and/or Probit Regression - Quick Tab

The Quick tab of the Logistic Regression (Logit) dialog box contains the options described here. Note that the options on this tab are identical to the options on the Probit Regression dialog box - Quick tab (accessed by selecting Quick Probit regression from the Nonlinear Estimation Startup Panel - Quick tab).

Input file contains. This drop-down list contains two options: Codes and counts and Codes and no counts. Use this option to specify the method for setting up the data file for logit or probit regression analysis. See Probit/Logit Regression - Input File for more detailed information about these options.

Variables. Click the Variables button to display the standard variable selection dialog box, in which you select one dependent variable and a set of independent or predictor variables. In addition, if you selected Codes and counts in the Input file contains drop-down list, also specify the Count variable containing the frequency counts.

Codes for dep. var. Logit and probit regression models, in a sense, predict probabilities underlying the dichotomous dependent variable, and these methods will produce predicted (expected) values in the range between 0 and 1 (for details, see Common Nonlinear Regression Models). If the dependent variable is not coded in this way, that is, as 0 and 1, then you must specify the respective codes in the Codes for dep. var. boxes. To do this, double-click on this field (or press the F2 key on your keyboard) to display the variable dialog box or simply type in the two codes. As the data are read, the dependent variable will then be transformed so that all values that match the first code become 0 (zero), and all values that match the second code become 1.

Note: Generalized Linear 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 can be used 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 Model (GLZ) module are more efficient, and Statistica only includes the models here for compatibility purposes.