Interpreting probability models : logit, probit and other generalized linear models
What is the probability that something will occur, and how is that probability altered by a change in an independent variable? To answer these questions, Tim Futing Liao introduces a systematic way of interpreting commonly used probability models
Quantitative applications in the social sciences, no. 07-101
Bibliography
1 online resource (vii, 88 pages) : illustrations
9780585216898, 9781412984577, 9781452209128, 0585216894, 1412984572, 145220912X
44960662
1. Introduction
Why probability models?
Why interpretation?
2. Generalized linear models and the interpretation of parameters
Generalized linear models
Interpretation of parameter estimates
3. Binary logit and probit models
Logit models
Interpretation of logit models
Probit models
Interpretation of probit models
Logit or probit models?
4. Sequential logit and probit models
The model
Interpretation of sequential logit and probit models
5. Ordinal logit and probit models
The model
Interpretation of ordinal logit and probit models
6. Multinomial logit models
The model
Interpretation of multinomial logit models
7. Conditional logit models
The model
Interpretation of conditional logit models
8. Poisson regression models
The model
Interpretation of poisson regression models
9. Conclusion
English
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