Modern Regression Methods
2nd Revised edition
By (author) Thomas P. Ryan
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Modern Regression Methods by Thomas P. Ryan
Book 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.
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Book DetailsISBN: 9780470081860
(242mm x 155mm x 35mm)
Publisher: John Wiley and Sons Ltd
Publish Date: 25-Nov-2008
Country of Publication: United States
Books By Author Thomas P. Ryan
Sample Size Determination and Power, Hardback (September 2013)
Featuring a comprehensive approach to sample size determination for general statistical use, this book uniquely blends applications from a variety of fields including statistics, biostatistics, the health sciences, and engineering.
Statistical Methods for Quality Improvement, Hardback (August 2011)
Statistical methods for quality improvement offer numerous benefits for industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems.
Modern Regression Methods, Hardback (June 2009)
This set contains ISBN 978-0-470-08186-0 Modern Regression Methods, 2nd Edition and the Solutions Manual 978-0-470-09606-2.
Modern Engineering Statistics, Paperback (November 2007)» View all books by Thomas P. Ryan
The objective of this book is to motivate an appreciation of contemporary statistical techniques within the context of engineering. The author presents an optimum blend between statistical thinking and statistical methodology through emphasis of a broad sweep of "tools" rather than endless streams of seemingly unrelated methods and formulae.
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Author Biography - Thomas P. Ryan
Thomas P. Ryan , PhD, served on the Editorial Review Board of the Journal of Quality Technology from 1990-2006, including three years as the book review editor. He is the author of four books, all of which are published by Wiley, and he is also an elected Fellow of the American Statistical Association, the American Society for Quality, and the Royal Statistical Society. A former consultant to Cytel Software Corporation, Dr. Ryan currently teaches advanced courses at statistics.com on the design of experiments, statistical process control, and engineering statistics.
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