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Linear probability, logit, and probit models

Author: John H Aldrich; Forrest D Nelson
Publisher: Beverly Hills, California : Sage Publications, ©1984.
Series: Quantitative applications in the social sciences, no. 07-045.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
After showing why ordinary regression analysis is not appropriate for investigating dichotomous or otherwise 'limited' dependent variables, this volume examines three techniques which are well suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models.
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Additional Physical Format: Online version:
Aldrich, John Herbert, 1947-
Linear probability, logit, and probit models.
Beverly Hills : Sage Publications, ©1984
(OCoLC)569369191
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: John H Aldrich; Forrest D Nelson
ISBN: 0803921330 9780803921337
OCLC Number: 11728938
Description: 95 pages : illustrations ; 22 cm.
Contents: 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.
Series Title: Quantitative applications in the social sciences, no. 07-045.
Responsibility: John H. Aldrich, Forrest D. Nelson.
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Abstract:

After showing why ordinary regression analysis is not appropriate for investigating dichotomous or otherwise 'limited' dependent variables, this volume examines three techniques which are well suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models.

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