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"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-Jurgen Schmidt (Potsdam), Zentralblatt MATH

Handbook of Regression Methods

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