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 mode...
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Newbury Park, California
SAGE Publications, Inc.
1984
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| Series: | Series: Quantitative Applications in the Social Sciences
Number 07-045 |
| Subjects: | |
| Online Access: | Click Here to View Status and Holdings. |
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Add Tag | 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. |
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| Physical Description: | 95 pages illustrations 22 cm |
| Bibliography: | Bibliography: pages 93-94 |
| ISBN: | 0803921330 |
Interpreting probability models logit, probit, and other generalized linear model