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.)

"...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

• 1. Introduction to Regression Models2. Estimating Regression Model Parameters3. The Classical Model and Its Consequences4. Evaluating Assumptions5. Transformations6. The Multiple Regression Model7. Multiple Regression from the Matrix Point of View8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity9. Polynomial Models and Interaction (Moderator) Analysis10. ANOVA, ANCOVA, and Other Applications of Indicator Variables11. Variable Selection12. Heteroscedasticity and Non-independence13. Models for Binary, Nominal, and Ordinal Response Variables14. Models for Poisson and Negative Binomial Response15. Censored Data Models16. Outliers, Identification, Problems, and Remedies (Good and Bad)17. Neural Network Regression18. Regression Trees19. Bookend

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.)

"...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

• 1. Introduction to Regression Models2. Estimating Regression Model Parameters3. The Classical Model and Its Consequences4. Evaluating Assumptions5. Transformations6. The Multiple Regression Model7. Multiple Regression from the Matrix Point of View8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity9. Polynomial Models and Interaction (Moderator) Analysis10. ANOVA, ANCOVA, and Other Applications of Indicator Variables11. Variable Selection12. Heteroscedasticity and Non-independence13. Models for Binary, Nominal, and Ordinal Response Variables14. Models for Poisson and Negative Binomial Response15. Censored Data Models16. Outliers, Identification, Problems, and Remedies (Good and Bad)17. Neural Network Regression18. Regression Trees19. Bookend