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INTRODUCTION TO LINEAR REGRESSION ANALYSIS
Sixth Edition
DOUGLAS C. MONTGOMERY
Arizona State University
School of Computing, Informatics, and Decision Systems Engineering
Tempe, AZ
ELIZABETH A. PECK
The Coca-Cola Company (retired)
Atlanta, GA
G. GEOFFREY VINING
Virginia Tech
Department of Statistics
Blacksburg, VA
This sixth edition first published 2021
© 2021 John Wiley & Sons, Inc.
Edition History John Wiley and Sons, Inc. (5e, 2012)
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Library of Congress Cataloging-in-Publication Data
Names: Montgomery, Douglas C., author. | Peck, Elizabeth A., 1953– author. | Vining, G. Geoffrey, 1954– author.
Title: Introduction to linear regression analysis / Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining.
Description: Fifth edition. | Hoboken, New Jersey : Wiley, [2020] | Series: Wiley series in probability and statistics | Includes bibliographical references and index.
Identifiers: LCCN 2020034055 (print) | LCCN 2020034056 (ebook) | ISBN 9781119578727 (hardback) | ISBN 9781119578741 (adobe pdf) | ISBN 9781119578758 (epub)
Subjects: LCSH: Regression analysis.
Classification: LCC QA278.2 .M65 2020 (print) | LCC QA278.2 (ebook) | DDC 519.5/36–dc23
LC record available at https://lccn.loc.gov/2020034055 LC ebook record available at https://lccn.loc.gov/2020034056
Cover Design: Wiley
Cover Images: Abstract marbled background, blue marbling wavy lines © oxygen/Getty Images, Linear Regression analysis graph Courtesy of Douglas C. Montgomery
PREFACE
Regression analysis is one of the most widely used techniques for analyzing multifactor data. Its broad appeal and usefulness result from the conceptually logical process of using an equation to express the relationship between a variable of interest (the response) and a set of related predictor variables. Regression analysis is also interesting theoretically because of elegant underlying mathematics and a well-developed statistical theory. Successful use of regression requires an appreciation of both the theory and the practical problems that typically arise when the technique is employed with real-world data.
This book is intended as a text for a basic course in regression analysis. It contains the standard topics for such courses and many of the newer ones as well. It blends both theory and application so that the reader will gain an understanding of the basic principles necessary to apply regression model-building techniques in a wide variety of application environments. The book began as an outgrowth of notes for a course in regression analysis taken by seniors and first-year graduate students in various fields of engineering, the chemical and physical sciences, statistics, mathematics, and management. We have also used the material in many seminars and industrial short courses for professional audiences. We assume that the reader has taken a first course in statistics and has familiarity with hypothesis tests and confidence intervals and the normal,
