Description
Book SynopsisOver the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one.
The American Statistician
The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems. (Computing Reviews)
Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in 2003. This new edition has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis.
The book provides a unique treatment of fundamental regression methods, su
Trade Review
"The book is to be praised in that it makes the reader aware of a large number of approaches to regression situations, and also to their possible pitfalls. It is thus an excellent basis for an experienced instructor to teach regression at different levels." (Springer, August 2010)
"This book, at the undergraduate level and even at the graduate level, will be rewarding reading for anyone interested in learning the nuances of regression analysis." (Mathmatical Reviews, January 2010)
"The exercises are interesting and thought-provoking throughout. If you liked the first edition, you will be pleased with this revision also." (International Statistical Review, August 2009)
"The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems." (Computing Reviews, July 2009)
"In this second edition, Ryan (author, editor, and educator) provides substantial updates and revisions of his popular text for statisticians to include new information on the most current advances and research in regression analysis" (SciTech Reviews, March 2009)
"One would be hard-pressed to find another text that rivals this one in terms of coverage of the regression literature." (The American Statistician, 2009)
"I strongly recommend the book as a reference for anyone teaching or using regression." (MAA Reviews, 2009)
"Highly recommended for those already trained in mathematics and statistics who want a good guide to current practice and issues in multiple regression techniques." (Journal of Biopharmaceutical Statistics, 2009)
Table of ContentsDiagnostics and Remedial Measures.
Regression with Matrix Algebra.
Introduction to Multiple Linear Regression.
Plots in Multiple Regression.
Transformations in Multiple Regression.
Selection of Regressors.
Polynomial and Trigonometric Terms.
Logistic Regression.
Nonparametric Regression.
Robust Regression.
Ridge Regression.
Nonlinear Regression.
Experimental Designs for Regression.
Applications of Regression.
Appendices.
Index.
