Praise for the Fourth Edition
"As with previous editions, the authors have produced a leading
textbook on regression."
?Journal of the American Statistical Association
A comprehensive and up-to-date introduction to the
fundamentals of regression analysis
Introduction to Linear Regression Analysis, Fifth Edition
continues to present both the conventional and less common uses of
linear regression in today?s cutting-edge scientific
research. The authors blend both theory and application to equip
readers with an understanding of the basic principles needed to
apply regression model-building techniques in various fields of
study, including engineering, management, and the health
sciences.
Following a general introduction to regression modeling,
including typical applications, a host of technical tools are
outlined such as basic inference procedures, introductory aspects
of model adequacy checking, and polynomial regression models and
their variations. The book then discusses how transformations and
weighted least squares can be used to resolve problems of model
inadequacy and also how to deal with influential observations. The
Fifth Edition features numerous newly added topics,
including:
A chapter on regression analysis of time series data that
presents the Durbin-Watson test and other techniques for detecting
autocorrelation as well as parameter estimation in time series
regression models
Regression models with random effects in addition to a
discussion on subsampling and the importance of the mixed
model
Tests on individual regression coefficients and subsets of
coefficients
Examples of current uses of simple linear regression models and
the use of multiple regression models for understanding patient
satisfaction data.
In addition to Minitab, SAS, and S-PLUS, the authors have
incorporated JMP and the freely available R software to illustrate
the discussed techniques and procedures in this new edition.
Numerous exercises have been added throughout, allowing readers to
test their understanding of the material.
Introduction to Linear Regression Analysis, Fifth Edition
is an excellent book for statistics and engineering courses on
regression at the upper-undergraduate and graduate levels. The book
also serves as a valuable, robust resource for professionals in the
fields of engineering, life and biological sciences, and the social
sciences.
Author Biography
DOUGLAS C. MONTGOMERY, PhD, is Regents Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery is a Fellow of the American Statistical Association, the American Society for Quality, the Royal Statistical Society, and the Institute of Industrial Engineers and has more than thirty years of academic and consulting experience. He has devoted his research to engineering statistics, specifically the design and analysis of experiments, statistical methods for process monitoring and optimization, and the analysis of time-oriented data. Dr. Montgomery is the coauthor of Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition and Introduction to Time Series Analysis and Forecasting, both published by Wiley. ELIZABETH A. PECK, PhD, is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia. G. GEOFFREY VINING, PhD, is Professor in the Department of Statistics at Virginia Polytechnic and State University. He has published extensively in his areas of research interest, which include experimental design and analysis for quality improvement, response surface methodology, and statistical process control. A Fellow of the American Statistical Association and the American Society for Quality, Dr. Vining is the coauthor of Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition (Wiley).