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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: |
Paperback |
| List price:
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£34.99 |
| Our price: |
£31.49 |
| Discount: |
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| You save:
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£3.50 |
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| ISBN 10: |
0803921330 |
| ISBN 13: |
9780803921337 |
| Availability: |
Usually dispatched within 1-3 weeks.
 Delivery
rates
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| Stock: |
Currently 0 available |
| Publisher: |
SAGE PUBLICATIONS INC |
| Pub. date: |
1 November, 1984 |
| Series: |
Quantitative Applications in the Social Sciences |
| 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: |
Returnable |
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