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Linear Probability, Logit, and Probit Models : Quantitative Applications in the Social Sciences - John Aldrich

Linear Probability, Logit, and Probit Models

Quantitative Applications in the Social Sciences

Paperback

Published: 1st February 1985
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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.

The Linear Probability Model
Specification of Nonlinear Probability Models
Estimation of Probit and Logit Models for Dichotomous Dependent Variables
Minimum Chi-Square Estimation and Polytomous Models Summary and Extensions
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9780803921337
ISBN-10: 0803921330
Series: Quantitative Applications in the Social Sciences
Audience: Tertiary; University or College
Format: Paperback
Language: English
Number Of Pages: 96
Published: 1st February 1985
Publisher: SAGE Publications Inc
Country of Publication: US
Dimensions (cm): 22.23 x 13.97  x 0.64
Weight (kg): 0.14