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: | , |
|---|---|
| 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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| 100 | 1 | # | |a Aldrich, John Herbert |c 1947- |e author |
| 245 | 1 | 0 | |a LINEAR PROBABILITY, LOGIT, AND PROBIT MODELS |c JOHN H. ALDRICH, FORRESDT D. NELSON |
| 264 | # | 1 | |a Newbury Park, California |b SAGE Publications, Inc. |c 1984 |
| 264 | # | 4 | |c ©1984 |
| 300 | # | # | |a 95 pages |b illustrations |c 22 cm |
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| 490 | 1 | # | |a Series: Quantitative Applications in the Social Sciences |v Number 07-045 |
| 504 | # | # | |a Bibliography: pages 93-94 |
| 520 | # | # | |a 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. |
| 650 | # | 0 | |a Probabilities |
| 650 | # | 0 | |a Probits |
| 650 | # | 0 | |a Logits |
| 700 | 1 | # | |a Nelson, Forrest D. |e author |
| 856 | 4 | 0 | |z Click Here to View Status and Holdings. |u https://opac.uitm.edu.my/opac/detailsPage/detailsHome.jsp?tid=275479 |
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Interpreting probability models logit, probit, and other generalized linear model