Samprit Chatterjee - Handbook of Regression Analysis With Applications in R

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A complete list of the titles in this series appears at the end of this volume.

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

Cover Design: Wiley

Cover Image: © Dmitriy Rybin/Shutterstock

Set in 10.82/12pt AGaramondPro by SPi Global, Chennai, India

Dedicated to everyone who labors in the field of statistics, whether they are students, teachers, researchers, or data analysts.

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.