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Full Description
"Over 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, such as diagnostics, transformations, robust regression, and ridge regression. Unifying key concepts and procedures, this new edition emphasizes applications to provide a more hands-on and comprehensive understanding of regression diagnostics. New features of the Second Edition include:
A revised chapter on logistic regression, including improved methods of parameter estimation
A new chapter focusing on additional topics of study in regression, including quantile regression, semiparametric regression, and Poisson regression
A wealth of new and updated exercises with worked solutions
An extensive FTP site complete with Minitab macros, which allow the reader to compute analyses, and specialized procedures
Updated references at the end of each chapter that direct the reader to the appropriate resources for further study
An accessible guide to state-of-the-art regression techniques, Modern Regression Methods, Second Edition is an excellent book for courses in regression analysis at the upper-undergraduate and graduate levels. It is also a valuable reference for practicing statisticians, engineers, and physical scientists.
Contents
Diagnostics 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.
