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Linear Probability, Logit, and Probit Models
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Linear Probability, Logit, and Probit Models



November 1984 | 96 pages | SAGE Publications, Inc

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


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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

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