In the previous section, we introduced the simple logistic regression model, where the dichotomous response depends on only one explanatory variable. As in the case of linear regression, which we analyzed in Chapter 2, Basic Concepts – Simple Linear Regression, and Chapter 3, More Than Just One Predictor – MLR, the popularity of a modeling technique lies in its ability to model many variables, which can be on different measurement scales. Now, we will generalize the logistic model to the case of more than one independent variable.
Central arguments in dealing with multiple logistic models will be the estimate of the coefficients in the model and the tests for their significance. This will follow the same lines as the univariate model already seen in the previous section. In multiple regression, the coefficients are called partial because they express the specific relationship that an independent variable has with the dependent variable net of the other independent...
