Summary
Contents
Ordinary regression analysis is not appropriate for investigating dichotomous or otherwise `limited' dependent variables, but this volume examines three techniques -- linear probability, probit, and logit models -- which are well-suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models. Using detailed examples, Aldrich and Nelson point out the differences among linear, logit, and probit models, and explain the assumptions associated with each.
Specification of Nonlinear Probability Models
Specification of Nonlinear Probability Models
1.0 Introduction
Regression analysis has become a standard statistical tool in the social sciences. Its popularity stems from several sources. It provides much explanatory power, especially due to its multivariate nature. The Gauss-Markov Theorem (Johnston, 1984) proves that it has some very desirable ...