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Linear Probability, Logit, and Probit Models

About Linear Probability, Logit, and Probit Models

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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  • Language:
  • English
  • ISBN:
  • 9780803921337
  • Binding:
  • Paperback
  • Pages:
  • 96
  • Published:
  • February 20, 1985
  • Dimensions:
  • 142x216x5 mm.
  • Weight:
  • 118 g.
Delivery: 1-2 weeks
Expected delivery: December 11, 2024

Description of Linear Probability, Logit, and Probit Models

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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