Regression Models for Categorical, Count and Related Variables: An Applied Approach
Hoffmann, John P.
Regression Models for Categorical, Count and Related Variables: An Applied Approach - Oakland University of California Press 2016 - 411p
1. Review of linear regression models
2. Categorical data and generalized linear models
3. Logistic and profit regression models
4. Ordered logistic and probit regression models
5. Multinomial logistic and probit regression models
6. Poisson and negative binomial regression models
7. Event history models
8. Regression models for longitudinal data
9. Multilevel regression models
10. Principal components and factor analysis
11. Appendix A : SAS, SPSS and R code for examples in chapters
12. Appendix B : using simulations to examine assumptions of OLS regression
13. Appendix C : working with missing data
9780520289291
Regression Analysis - Mathematical Models
519.536 / HOF
Regression Models for Categorical, Count and Related Variables: An Applied Approach - Oakland University of California Press 2016 - 411p
1. Review of linear regression models
2. Categorical data and generalized linear models
3. Logistic and profit regression models
4. Ordered logistic and probit regression models
5. Multinomial logistic and probit regression models
6. Poisson and negative binomial regression models
7. Event history models
8. Regression models for longitudinal data
9. Multilevel regression models
10. Principal components and factor analysis
11. Appendix A : SAS, SPSS and R code for examples in chapters
12. Appendix B : using simulations to examine assumptions of OLS regression
13. Appendix C : working with missing data
9780520289291
Regression Analysis - Mathematical Models
519.536 / HOF