Jump quickly to results on these stores:
Understanding Regression Analysis: An Introductory Guide (Quantitative Applications in the Social Sciences) | Interaction Effects in Multiple Regression | Multiple Regression in Practice | Logistic Regression | Regression Models for Categorical and Limited Dependent Variables | Maximum Likelihood Estimation | Analysis of Nominal Data | Interpreting Probability Models | Regression Models for Categorical, Count, and Related Variables
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.
About: 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.
Pricing is shown for items sent to or within the U.S., excluding shipping and tax. Please consult the store to determine exact fees. No warranties are made express or implied about the accuracy, timeliness, merit, or value of the information provided. Information subject to change without notice. isbn.nu is not a bookseller, just an information source.