Regression Models For Categorical, Count, And Related Variables
Subtitle: An Applied Approach
Publisher: University of California Press
Published: August 16, 2016
Place of publication: Oakland, California, USA
Language: eng
Format: Paperback; Hardcover
Number of pages: 432
ISBN-13: 9780520289291
Copyright date: ©2016
Description: Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis.
Notes: Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data.
Includes bibliographical references (pages 397-401) and index.
Table of contents: title: -Review of linear regression models --; title: -Categorical data and generalized linear models --; title: -Logistic and probit regression models --; title: -Ordered logistic and probit regression models --; title: -Multinomial logistic and probit regression models --; title: -Poisson and negative binomial regression models --; title: -Event history models --; title: -Regression models for longitudinal data --; title: -Multilevel regression models --; title: -Principal components and factor analysis --; title: -Appendix A : SAS, SPSS, and R code for examples in chapters --; title: -Appendix B : using simulations to examine assumptions of OLS regression --; title: -Appendix C : working with missing data.