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Item Details
| Title:
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LINEAR PROBABILITY, LOGIT, AND PROBIT MODELS
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| By: |
John Aldrich, Forrest D. Nelson |
| Format: |
Electronic book text |
| List price:
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£16.79 |
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We currently do not stock this item, please contact the publisher directly for
further information.
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| ISBN 10: |
1452211108 |
| ISBN 13: |
9781452211107 |
| Publisher: |
SAGE PUBLICATIONS INC EBOOKS |
| Pub. date: |
1 November, 1984 |
| Series: |
Quantitative Applications in the Social Sciences 45 |
| Pages: |
96 |
| Description: |
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. |
| Synopsis: |
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. |
| Publication: |
US |
| Imprint: |
SAGE Publications Inc |
| Returns: |
Non-returnable |
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