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Handbook of Regression Analysis With Applications in R
Second Edition
Samprit Chatterjee
New York University, New York, USA
Jeffrey S. Simonoff
New York University, New York, USA
This second edition first published 2020
© 2020 John Wiley & Sons, Inc
Edition History
Wiley‐Blackwell (1e, 2013)
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Library of Congress Cataloging‐in‐Publication Data
Names: Chatterjee, Samprit, 1938- author. | Simonoff, Jeffrey S., author.
Title: Handbook of regression analysis with applications in R / Professor
Samprit Chatterjee, New York University, Professor Jeffrey S. Simonoff,
New York University.
Other titles: Handbook of regression analysis
Description: Second edition. | Hoboken, NJ : Wiley, 2020. | Series: Wiley
series in probability and statistics | Revised edition of: Handbook of
regression analysis. 2013. | Includes bibliographical references and
index.
Identifiers: LCCN 2020006580 (print) | LCCN 2020006581 (ebook) | ISBN
9781119392378 (hardback) | ISBN 9781119392477 (adobe pdf) | ISBN
9781119392484 (epub)
Subjects: LCSH: Regression analysis--Handbooks, manuals, etc. | R (Computer
program language)
Classification: LCC QA278.2 .C498 2020 (print) | LCC QA278.2 (ebook) |
DDC 519.5/36--dc23
LC record available at https://lccn.loc.gov/2020006580
LC ebook record available at https://lccn.loc.gov/2020006581
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Dedicated to everyone who labors in the field of statistics, whether they are students, teachers, researchers, or data analysts.
Preface to the Second Edition
The years since the first edition of this book appeared have been fast‐moving in the world of data analysis and statistics. Algorithmically‐based methods operating under the banner of machine learning, artificial intelligence, or data science have come to the forefront of public perceptions about how to analyze data, and more than a few pundits have predicted the demise of classic statistical modeling.
To paraphrase Mark Twain, we believe that reports of the (impending) death of statistical modeling in general, and regression modeling in particular, are exaggerated. The great advantage that statistical models have over “black box” algorithms is that in addition to effective prediction, their transparency also provides guidance about the actual underlying process (which is crucial for decision making), and affords the possibilities of making inferences and distinguishing real effects from random variation based on those models. There have been laudable attempts to encourage making machine learning algorithms interpretable in the ways regression models are (Rudin, 2019), but we believe that models based on statistical considerations and principles will have a place in the analyst's toolkit for a long time to come.
Of course, part of that usefulness comes from the ability to generalize regression models to more complex situations, and that is the thrust of the changes in this new edition. One thing that hasn't changed is the philosophy behind the book, and our recommendations on how it can be best used, and we encourage the reader to refer to the preface to the first edition for guidance on those points. There have been small changes to the original chapters, and broad descriptions of those chapters can also be found in the preface to the first edition. The five new chapters (