Modern Regression Methods
Published on 19. December 1996
Book
Hardback
536 pages
978-0-471-52912-5 (ISBN)
Description
Regression is a common statistical technique. This synthesis of the state-of-the-art regression methodology features a data analysis orientation and comprehensive treatment of regression diagnostics.
Reviews / Votes
"...an excellent book...worthwhile for anyone that uses regression techniques to obtain their own copy of this book..." (Australian & New Zealand J Statistics, 2000) "In this book on modern regression methods, Ryan presents "state-of-the-art" regression concepts. Highly recommended for upper-division undergraduates, graduate students, faculty and professionals." "This textbook provides a very thorough treatment of regression approaches, including techniques not covered in many books... This textbook is quite useful and provides excellent references... I would definitely recommend purchasing the book for research and teaching purposes." (Technometrics, Vol. 39, No. 4, Nov. 1997) "...has considerable potential as an undergraduate text for regression...also deep enough to be used as a reference on regression topics for the practicing statistician..." (Journal of the American Statistical Association) "...surprisingly easy to read...highly recommended for use as a textbook..." (Chance, Summer 2003)More details
Series
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
Illustrations
Dimensions
Height: 160 mm
Width: 240 mm
Weight
879 gr
ISBN-13
978-0-471-52912-5 (9780471529125)
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Schweitzer Classification
Other editions
New editions
Thomas P. Ryan
Modern Regression Methods
Book
11/2008
2nd Edition
Wiley
€170.50
Shipment within 10-20 days
Person
THOMAS P. RYAN is Professor of Statistics at the University of Newcastle in New South Wales, Australia, and the author of Statistical Methods for Quality Improvement, also published by Wiley.
Content
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.