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Linear probability, logit, and probit models [electronic resource] / John H. Aldrich, Forrest D. Nelson.

By: Aldrich, John Herbert, 1947-.
Contributor(s): Nelson, Forrest D.
Material type: TextTextSeries: Quantitative applications in the social sciences: no. 07-045.Publisher: Beverly Hills : Sage Publications, c1984Description: 1 online resource (95 p.) : ill.ISBN: 0585216932 (electronic bk.); 9780585216935 (electronic bk.); 9781412984744 (ebook); 1412984742 (ebook).Subject(s): Probabilities | Logits | Probits | Probability | Logistic Models | Multivariate Analysis | Social Sciences -- methods -- Statistics | Probabilit�es | Logits | Probits | MATHEMATICS -- Probability & Statistics -- General | Waarschijnlijkheidstheorie | Logits | Probits | Sociale wetenschappen | Social sciences Regression analysisGenre/Form: Electronic books.Additional physical formats: Print version:: Linear probability, logit, and probit models.DDC classification: 519.2 Other classification: 31.70 Online resources: EBSCOhost
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 -- Minimum chi-square estimation and polytomous models -- Summary and extensions.
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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Includes bibliographical references (p. 93-94).

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 -- Minimum chi-square estimation and polytomous models -- Summary and extensions.

Description based on print version record.

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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Linear probability, logit, and probit models by Aldrich, John Herbert, ©1984
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