Sage University Paper in the series: Quanitative Applications in the Social Sciences. Series editors: John L. Sullivan, University of Minnesota and Richard G. Niemi, University of Rochester. N� de ref. del art�culo 056390

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T�tulo: Linear Probability, Logit, and Probit Models

Editorial: SAGE Publications, Newbury Park

A�o de publicaci�n: 1984

Encuadernaci�n: Trade Paperback

Condici�n: Very Good

Condici�n de la sobrecubierta: wraps

Edici�n: First

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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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