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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.
Features
-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
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Subjects
Regression analysis, Multivariate analysis, Linear models, Mathematical statistics, Statistical inference, Data Mining, Regression Analysis, Multivariate Analysis, Statistical Theory & Methods, Statistical Computing, Analyse de régression, Analyse multivariée, MATHEMATICS, Applied, Probability & Statistics, General, Multivariate Analyse, RegressionsanalyseShowing 5 featured editions. View all 5 editions?
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Handbook of Regression Methods: 1st edition
July 5, 2017, Chapman and Hall, CRC Press
Hardcover
in English
1498775292 9781498775298
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Book Details
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Edition Notes
A Chapman & Hall Book.
Includes bibliographical references and index.
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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.
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- Created March 10, 2019
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December 15, 2022 | Edited by MARC Bot | import existing book |
September 20, 2021 | Edited by ImportBot | import existing book |
March 10, 2019 | Edited by Kaustubh Chakraborty | Added new cover |
March 10, 2019 | Edited by Kaustubh Chakraborty | Added new book |
March 10, 2019 | Created by Kaustubh Chakraborty | Added new book. |