Regression models for categorical, count, and related variables : an applied approach

Regression models for categorical, count, and related variables : an applied approach /

"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists...

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Bibliographic Details
Online Access: Full text (MCPHS users only)
Main Author: Hoffmann, John P. (John Patrick), 1962- (Author)
Format: Electronic eBook
Language:English
Published: Oakland, California : University of California Press, 2016
Subjects:
Regression analysis > Mathematical models.
Regression analysis > Computer programs.
Social sciences > Statistical methods.
Local Note:ProQuest Ebook Central
Table of Contents:
  • Review of linear regression models
  • Categorical data and generalized linear models
  • Logistic and probit regression models
  • Ordered logistic and probit regression models
  • Multinomial logistic and probit regression models
  • Poisson and negative binomial regression models
  • Event history models
  • Regression models for longitudinal data
  • Multilevel regression models
  • Principal components and factor analysis
  • Appendix A : SAS, SPSS, and R code for examples in chapters
  • Appendix B : using simulations to examine assumptions of OLS regression
  • Appendix C : working with missing data.

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