Introduction to Linear Regression Analysis

By Elizabeth A Peck, Douglas C. Montgomery, G Geoffrey Vining, et al.

Book Overview

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.

• Format: Hardcover
• Language: English
• ISBN: 0470542810
• ISBN13: 9780470542811
• Release Date: April 2012
• Publisher: Wiley
• Length: 672 Pages
• Weight: 3.35 lbs.
• Dimensions: 1.5" x 7.1" x 10.1"

Customer Reviews

excellent

it saids the book is used but like new. However, the book is actually totally new, never used before,no pollution at all. The price is also one of the lowest compare with others. Excellent!!

For Self Study Get An Earlier Edition

I have access to this, the third edition and the latest, the fourth edition, through my company's library. There is really no material difference in the content and I was able to save about 80% of the purchase price by buying a used copy of the third edition, vs. new copy of fourth edition. Wonderful book for self study. You will benefit most if you have a good background in probability theory and linear algebra and want to understand the details and language of linear regression. Even without that background chapters one through three will teach you more than you will ever learn in most survey courses in statistics. To fully appreciate the whole book I think you need a one semester course in linear algebra and one or two semesters of probability theory.

Good book

This is a good book with good exercises in the end of the chapters, but a little hard to read.

A good book with industrial applications

very useful for industrial applications. There are quite a few printing mistakes and that would be a problem for those reader they are not very strong in statistics.

Excellent introduction to linear regression

If you have a desire or need to develop regression models, whether for prediction or classification, this is a great place to start climbing the learning curve. The book covers all the essentials, such as how to fit a model to a set of data, how to evaluate the quality of the fit, and how to detect influential data points. It also does a good job with some of the issues involved in fitting a regression (most notably colinearity, overfitting, outliers, and deviations from normality) and discusses ridge regression, principal components regression, and other so-called "robust" methods for dealing with such issues. Even if you plan to use nonlinear modelling techniques like polynomial regression or feed-forward neural networks, this book is worth reading: many of the same issues that are involved when developing linear regression models arise in the context of nonlinear models. I use multivariate polynomial regression models for pricing options, and cite this book in my own recent work on that subject--"Advanced Option Pricing Models" (McGraw Hill, Feb 2005). Jeffrey Owen Katz, Ph.D. Author (with Donna L. McCormick) of "The Encyclopedia of Trading Strategies" (McGraw Hill, 2000).