Handbook of Regression Methods: 1st Edition (Hardback) book cover

Handbook of Regression Methods

1st Edition

By Derek Scott Young

Chapman and Hall/CRC

638 pages | 50 B/W Illus.

Purchasing Options:$ = USD
Hardback: 9781498775298
pub: 2017-07-05

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Handbook of Regression Methods concisely covers numerous traditional, contemporary, and nonstandard regression methods. The handbook provides a broad overview of regression models, diagnostic procedures, and inference procedures, with emphasis on how these methods are applied. The organization of the handbook benefits both practitioners and researchers, who seek either to obtain a quick understanding of regression methods for specialized problems or to expand their own breadth of knowledge of regression topics.

This handbook covers classic material about simple linear regression and multiple linear regression, including assumptions, effective visualizations, and inference procedures. It presents an overview of advanced diagnostic tests, remedial strategies, and model selection procedures. Finally, many chapters are devoted to a diverse range of topics, including censored regression, nonlinear regression, generalized linear models, and semiparametric regression.


  • Presents a concise overview of a wide range of regression topics not usually covered in a single text
  • Includes over 80 examples using nearly 70 real datasets, with results obtained using R
  • Offers a Shiny app containing all examples, thus allowing access to the source code and the ability to interact with the analyses


"Covering a wide range of regression topics, this clearly written handbook explores not only the essentials of regression methods for practitioners but also a broader spectrum of regression topics for researchers. Complete and detailed, this unique, comprehensive resource provides an extensive breadth of topical coverage, some of which is not typically found in a standard text on this topic. Young (Univ. of Kentucky) covers such topics as regression models for censored data, count regression models, nonlinear regression models, and nonparametric regression models with autocorrelated data. In addition, assumptions and applications of linear models as well as diagnostic tools and remedial strategies to assess them are addressed. Numerous examples using over 75 real data sets are included, and visualizations using R are used extensively. Also included is a useful Shiny app learning tool; based on the R code and developed specifically for this handbook, it is available online. This thoroughly practical guide will be invaluable for graduate collections."~D. J. Gougeon, Choice Connect

"The list of calculated examples contains virtually every possible field of application of statistics, a small subset of them reads as follows: car sale data, cheese-tasting experiment data, credit loss data, hospital stays data, James Bond data, and wind direction data." ~Hans-Jurgen Schmidt (Potsdam), Zentralblatt MATH

Table of Contents

Introduction. Simple Linear Regression. The Basics of Regression Models. Statistical Inference. Statistical Intervals. Assessing Regression Assumptions. ANOVA I. Multiple Linear Regression. Multiple Regression. Matrix Notation in Regression. Indicator Variables. Multicollinearity. ANOVA II. Advanced Regression Diagnostic Methods. Influential Data Values. Measurement Errors and Instrumental Variables Regression. Weighted Least Squares and Robust Regression Procedures. Correlated Errors and Autoregressive Structures. Crossvalidation and Model Selection Methods. Advanced Regression Models. Biased Regression Methods and Regression Shrinkage. Piecewise and Nonparametric Methods. Regression Models with Censored Data. Nonlinear Regression. Regression Models with Counts as Responses. Multivariate Multiple Regression. Data Mining. Miscellaneous Topics. Appendices.

About the Author

Derek Young is an assistant professor of statistics at the University of Kentucky. He has over ten years of experience as a statistician, including positions in industry, government, and academia. During this time, he has also taught online courses in regression methods for Penn State University and the University of Kentucky. His research interests include (finite) mixture models, tolerance regions, and statistical computing.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General