About this title: 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.
Note: This is a general synopsis. Each listing is described below.
Binding: Paperback
Publisher: Sage Publications, Inc
Date Published: 1984
ISBN-13:9780803921337ISBN:0803921330
Description: Good. Unmarked text. Series paper #45. Previous owner's name on front end page. References. 95pp. Notes. After showing why ordinary regression analysis is not appropriate for investigating dichotomous or otherwise "limited" dependent variables, 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. read more
Binding: Paperback
Publisher: SAGE PUBN
Date Published: 1984
ISBN-13:9780803921337ISBN:0803921330
Description: New. 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. read more
Description: New. 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. ISBN10: 0803921330. read more
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