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Applied Regression Modeling
Third Edition
Iain Pardoe Thompson Rivers University The Pennsylvania State University
Copyright
This edition first published 2021
© 2021 John Wiley & Sons, Inc
Edition History
Second Edition, 2012, John Wiley & Sons, Inc
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Library of Congress Cataloging‐in‐Publication Data
Names: Pardoe, Iain, 1970- author
Title: Applied regression modeling / Iain Pardoe, Thompson Rivers
University, The Pennsylvania State University.
Description: Third edition. | Hoboken, New Jersey : Wiley, [2020] |
includes bibliographical references and index.
Identifiers: LCCN 2020028117 (print) | LCCN 2020028118 (ebook) | ISBN
9781119615866 (cloth) | ISBN 9781119615880 (adobe pdf) | ISBN
9781119615903 (epub)
Subjects: LCSH: Regression analysis. | Statistics.
Classification: LCC QA278.2 .P363 2020 (print) | LCC QA278.2 (ebook) |
DDC 519.5/36–dc23
LC record available at https://lccn.loc.gov/2020028117
LC ebook record available at https://lccn.loc.gov/2020028118
Cover Design: Wiley
Cover Image: Courtesy of Bethany Pardoe
To Bethany and Sierra
Preface
The first edition of this book was developed from class notes written for an applied regression course taken primarily by undergraduate business majors in their junior year at the University of Oregon. Since the regression methods and techniques covered in the book have broad application in many fields, not just business, the second edition widened its scope to reflect this. This third edition refines and improves the text further. Details of the major changes for the third edition are included at the end of this preface.
The book is suitable for any undergraduate or graduate statistics course in which regression analysis is the main focus. A recommended prerequisite is an introductory probability and statistics course. It is also appropriate for use in an applied regression course for MBAs and for vocational, professional, or other non‐degree courses. Mathematical details have deliberately been kept to a minimum, and the book does not contain any calculus. Instead, emphasis is placed on applying regression analysis to data using statistical software, and understanding and interpreting results. Optional formulas are provided for those wishing to see these details and the book now includes an informal overview of matrices in the context of multiple linear regression.