Regression models for categorical dependent variables using Stata
After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models and describes models for sample selection bias.
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- Full title :Regression models for categorical dependent variables using Stata / J. Scott Long, Jeremy Freese.
Summary
After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models and describes models for sample selection bias.
Read More ↓Edition Statements
- Publication Date:2014.
- Edition:3rd ed
Publication Statements
- Publisher(s):Stata Press Publication, StataCorp LP
- Place of Publication:College Station, Texas
- Language:eng
- Publication Date:2014.
The Physical Object
- Description:xxiii, 589 p. : : ill
Edition Identifiers
- ISBN:9781597181112, 1597181110
- oclc:ocn902663004
- LC Classification:QA278 .2 L848r 2014
- Dewey Decimal:519.536
Table of Contents
Introduction -- Introduction to Stata -- Estimation, testing, and fit -- Methods of interpretation -- Models for binary outcomes : estimation, testing, and fit -- Models for binary outcomes : interpretation -- Models for ordinal outcomes -- Models for nominal outcomes -- Models for count outcomes.
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