Regression Modeling and Data Analysis with Applications in R (Wiley Series in Probability and Statistics) - Hardcover

Chatterjee, Samprit; Simonoff, Jeffrey S.

Hardcover

ISBN 10: 1119392373 ISBN 13: 9781119392378

Publisher: Wiley, 2020

Synopsis

Handbook and reference guide for students and practitioners of statistical regression-based analyses in R

Handbook of Regression Analysis with Applications in R, Second Edition 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.

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:

• Regularization methods
• Smoothing methods
• Tree-based methods

In the new edition of the Handbook, 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.

Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.

About this title

About the Author

Samprit Chatterjee, PhD, 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 Regression Analysis By Example, Sensitivity Analysis in Linear Regression, A Casebook for a First Course in Statistics and Data Analysis, and the first edition of Handbook of Regression Analysis, all published by Wiley.

Jeffrey S. Simonoff, PhD, 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.

From the Back Cover

Handbook and reference guide for students and practitioners of statistical regression-based analyses in R

Handbook of Regression Analysis with Applications in R, Second Edition 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.

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:

• Regularization methods
• Smoothing methods
• Tree-based methods

In the new edition of the Handbook, 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.

Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.

From the Inside Flap

Handbook and reference guide for students and practitioners of statistical regression-based analyses in R

Handbook of Regression Analysis with Applications in R, Second Edition 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.

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:

• Regularization methods
• Smoothing methods
• Tree-based methods

In the new edition of the Handbook, 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.

Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.

Edition details

• Publisher: Wiley
• Publication date: 2020
• Language: English
• ISBN 10: 1119392373
• ISBN 13: 9781119392378
• Binding: Hardcover
• Edition number: 2
• Number of pages: 384