Linear Probability, Logit, and Probit Models
By: Aldrich, John H
| Nelson, Forrest D.
Material type: 
BookPublisher: Thousand Oaks, CA, SAGE Publications, 1984Description: 95 pages.ISBN: 978-0-8039-2133-7.Subject(s): regression analysis | equations | models | modeling | probability models | social sciencesOnline resources: Publisher's website Summary: 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.
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Monography | Library | C2 14 (Browse shelf) | Available | 215 |
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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