Handbook of Regression Analysis with Applications in R - (Wiley Probability and Statistics) 2nd Edition by Samprit Chatterjee & Jeffrey S Simonoff
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<p/><br></br><p><b> About the Book </b></p></br></br>"Building on the Handbook of Regression Analysis and Regression Analysis by Example, the authors' thorough treatments of "classic" regression analysis, this book covers two important and more advanced topics of time-to-event survival data and longitudinal and clustered data. Further, methods that have become prominent in the last 15-30 years that are designed for analyses on often-large data sets and can take advantage of exibility in modeling were not covered, including smoothing, tree- based, and regularization methods, all of which are increasingly becoming part of the data analysis toolkit. Examples are drawn from a wide variety of application areas using real data sets and all of the R code is provided. The book will be of interest to data scientists as well as in regression analysis courses at the graduate and undergraduate level. Regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables -- that is, the average value of the dependent variable when the independent variables are fixed. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning"--<p/><br></br><p><b> Book Synopsis </b></p></br></br><p><b>H</b><b>andbook and reference guide for students and practitioners of statistical regression-based analyses in R</b> </p> <p><i>Handbook of Regression Analysis </i><i>with Applications in R, Second Edition </i>is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors' thorough treatment of "classical" regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data. </p> <p>The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include: </p> <ul> Regularization methods </li> Smoothing methods </li> Tree-based methods </li> </ul> <p>In the new edition of the <i>Handbook</i>, the data analyst's toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website. </p> <p>Of interest to undergraduate and graduate students taking courses in statistics and regression, the <i>Handbook of Regression Analysis </i>will also be invaluable to practicing data scientists and statisticians. </p><p/><br></br><p><b> From the Back Cover </b></p></br></br><p><b>Handbook and reference guide for students and practitioners of statistical regression-based analyses in R</b> <p><i>Handbook of Regression Analysis with Applications in R, Second Edition</i> is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors' thorough treatment of "classical" regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data. <p>The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include: <ul> <li>Regularization methods</li> <li>Smoothing methods</li> <li>Tree-based methods</li> </ul> <p>In the new edition of the <i>Handbook</i>, the data analyst's toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website. <p>Of interest to undergraduate and graduate students taking courses in statistics and regression, the <i>Handbook of Regression Analysis</i> will also be invaluable to practicing data scientists and statisticians.<p/><br></br><p><b> About the Author </b></p></br></br><p><b>Samprit Chatterjee, PhD, </b> is Professor Emeritus of Statistics at New York University. A Fellow of the American Statistical Association, Dr. Chatterjee has been a Fulbright scholar in both Kazakhstan and Mongolia. He is the coauthor of multiple editions of <i>Regression Analysis By Example</i>, <i>Sensitivity Analysis in Linear Regression</i>, <i>A Casebook for a First Course in Statistics and Data Analysis</i>, and the first edition of <i>Handbook of Regression Analysis</i>, all published by Wiley. <p><b>Jeffrey S. Simonoff, PhD, </b> is Professor of Statistics at the Leonard N. Stern School of Business of New York University. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. He has authored, coauthored, or coedited more than one hundred articles and seven books on the theory and applications of statistics.
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