Linear Probability, Logit, and Probit Models

By: John Aldrich, Forrest D. Nelson

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Paperback | 1 November 1984

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96 Pages

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16.5 x 9.5 x 1

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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.

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