Description
Book SynopsisUnderstanding Regression Analysis 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. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways.
Key features of the book include:
- Numerous worked examples using the R software
- Key points and self-study questions displayed just-in-time within chapters
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Trade Review
"...The authors suggest their book is suitable for those who are “research-oriented”, regardless of any prior advanced training in statistics...I particularly like the emphasis on assumptions. Rather than discuss regression in idealized terms, Westfall and Arias are upfront about why assumptions are often wrong in practice, and what an analyst can do about violations. These discussions are woven into many of the chapters, and in some cases, they are featured in stand-alone chapters...I am a fan of learning statistics by doing, so the large amount of R code woven into the book’s chapters and the hands-on exercises at the end of each chapter are valuable and a welcomed feature of the book...To me, this textbook would be most suitable for a one-semester survey course in statistical methods for students outside of biostatistics or statistics. A motivated student could even use this book for self-study...Overall, I believe this is a worthwhile addition to the literature."
- Ryan Andrews, ISCB News, June 2021
Table of Contents
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
