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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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
College Station, Texas :
Stata Press,
2014.
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| Edition: | Third edition. |
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| 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. "The goal of Regression Models for Categorical Dependent Variables Using Stata, Third Edition is to make it easier to carry out the computations necessary to fully interpret regression models for categorical outcomes by using Stata's margins command. Because the models are nonlinear, they are more complex to interpret. Most software packages that fit these models do not provide options that make it simple to compute the quantities useful for interpretation. In this book, the authors briefly describe the statistical issues involved in interpretation, and then they show how you can use Stata to perform these computations."--Back cover. |
Hesburgh Library General Collection
| Call Number: |
QA 278.2 .L66 2014
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