This book unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks and decision trees under a common umbrella; namely, the conditional distribution model.
This book unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks and decision trees under a common umbrella; namely, the conditional distribution model.
Peter H. Westfall has a Ph.D. in Statistics from the University of California at Davis, as well as many years of teaching, research, and consulting experience, in a variety of statistics-related disciplines. He has published over 100 papers on statistical theory, methods, and applications; and he has written several books, spanning academic, practitioner, and textbook genres. He is former editor of The American Statistician, and a Fellow of the American Statistical Association. Andrea L. Arias is a Senior Operations Research Specialist at BNSF Railway. She has a Ph.D. in Industrial Engineering with a minor in Business Statistics from Texas Tech University, and a Doctoral Degree in Industrial Engineering from Pontificia Universidad Católica de Valparaiso, Chile. Her main areas of expertise include Mathematical Programming, Network Optimization, Statistics and Simulation. She is an active member of the Institute for Operations Research and the Management Sciences (INFORMS.)
Inhaltsangabe
1. Introduction to Regression Models 2. Estimating Regression Model Parameters 3. The Classical Model and Its Consequences 4. Evaluating Assumptions 5. Transformations 6. The Multiple Regression Model 7. Multiple Regression from the Matrix Point of View 8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity 9. Polynomial Models and Interaction (Moderator) Analysis 10. ANOVA, ANCOVA, and Other Applications of Indicator Variables 11. Variable Selection 12. Heteroscedasticity and Non-independence 13. Models for Binary, Nominal, and Ordinal Response Variables 14. Models for Poisson and Negative Binomial Response 15. Censored Data Models 16. Outliers, Identification, Problems, and Remedies (Good and Bad) 17. Neural Network Regression 18. Regression Trees 19. Bookend
1. Introduction to Regression Models 2. Estimating Regression Model Parameters 3. The Classical Model and Its Consequences 4. Evaluating Assumptions 5. Transformations 6. The Multiple Regression Model 7. Multiple Regression from the Matrix Point of View 8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity 9. Polynomial Models and Interaction (Moderator) Analysis 10. ANOVA, ANCOVA, and Other Applications of Indicator Variables 11. Variable Selection 12. Heteroscedasticity and Non-independence 13. Models for Binary, Nominal, and Ordinal Response Variables 14. Models for Poisson and Negative Binomial Response 15. Censored Data Models 16. Outliers, Identification, Problems, and Remedies (Good and Bad) 17. Neural Network Regression 18. Regression Trees 19. Bookend
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