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## Regression Models for Categorical Dependent Variables Using Stata, Second Edition

$58.00 each  Authors: J. Scott Long and Jeremy Freese Publisher: Stata Press Copyright: 2006 ISBN-13: 978-1-59718-011-5 Pages: 527; paperback Price:$58.00

### Comment from the Stata technical group

Regression Models for Categorical Dependent Variables Using Stata, Second Edition, by J. Scott Long and Jeremy Freese, shows how to use Stata to fit and interpret regression models for categorical data. Nearly 50% longer than the previous edition, the second edition covers new topics for fitting and interpreting models included in Stata 9, such as multinomial probit models, the stereotype logistic model, and zero-truncated count models. Many of the interpretation techniques have been updated to include interval and point estimates.

Although regression models for categorical dependent variables are common, few texts explain how to interpret such models; Regression Models for Categorical Dependent Variables Using Stata, Second Edition fills this void. To accompany the book, Long and Freese provide a suite of commands for hypothesis testing and model diagnostics.

The second edition begins with an excellent introduction to Stata and follows with general treatments of estimation, testing, fit, and interpretation in this class of models. Long and Freese detail binary, ordinal, nominal, and count outcomes in separate chapters. The final chapter explains how to fit and interpret models with special characteristics, such as interaction, nonlinear terms, and ordinal and nominal independent variables. One appendix explains the syntax of the author-written commands, and a second appendix details the book's datasets.

Long and Freese use many concrete examples in their second edition. All the examples, datasets, and author-written commands are available on the authors’ website, so readers can easily replicate the examples when using Stata. This book is ideal for students or applied researchers who want to learn how to fit and interpret models for categorical data.

### Table of contents

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