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.
| Основен автор: | Liao, Tim Futing. |
|---|---|
| Формат: | Електронна книга |
| Език: | English |
| Публикувано: |
Thousand Oaks, Calif. :
Sage,
℗♭1994.
|
| Серия: |
Quantitative applications in the social sciences ;
no. 07-101. |
| Предмети: | |
| Онлайн достъп: |
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=24726 |
| Подобни документи: |
Print version::
Interpreting probability models. |
Съдържание:
- 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.