Introduction to Linear Regression Analysis / Edition 4

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Overview

Covers both theory and application so the reader can understand the basic principles and apply regression methods in a variety of practical settings. Revisions include new material on regression diagnostics, more sample computer output with expanded interpretations, a discussion on handling missing observations and introductions to handling generalized linear models and nonlinear regression.
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Editorial Reviews

SciTech Book
...[the authors] describe conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research.
SciTech Book
...[the authors] describe conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research.
Booknews
New edition of a text on regression analysis, a statistical technique for investigating and modeling the relationship between variables. Montgomery (industrial engineering, Arizona State U.), Elizabeth A. Peck (logistics modeling specialist, Coca-Cola Co.) and G. Geoffrey Vining (statistics, Virginia Tech) describe conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction, they outline a host of technical tools including basic inference procedures and introductory aspects of model adequacy checking, simple and multiple linear regression, model adequacy checking, transformations and weighting to correct model inadequacies, diagnostics for leverage and influence, polynomial regression models, indicator variables, variable selection and model building, multicollinearity, robust regression, generalized linear models, nonlinear regression, validation of regression models, and other topics. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher
“The book can be used for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. It also serves as a resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.” (Zentralblatt MATH, 1 October 2013)
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Product Details

  • ISBN-13: 9780471754954
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 7/21/2006
  • Series: Wiley Series in Probability and Statistics Series , #615
  • Edition description: Revised Edition
  • Edition number: 4
  • Pages: 640
  • Product dimensions: 7.00 (w) x 10.00 (h) x 1.38 (d)

Meet the Author

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).

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Table of Contents

1 Introduction 1
2 Simple linear regression 12
3 Multiple linear regression 63
4 Model adequacy checking 122
5 Transformations and weighting to correct model inadequacies 160
6 Diagnostics for leverage and influence 189
7 Polynomial regression models 201
8 Indicator variables 237
9 Variable selection and model building 261
10 Validation of regression models 305
11 Multicollinearity 323
12 Robust regression 369
13 Introduction to nonlinear regression 397
14 Generalized linear models 427
15 Other topics in the use of regression analysis 475
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