Items related to Handbook of Regression Methods
Synopsis
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
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
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Handbook of Regression Methods (Hardcover)
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ISBN 10: 1498775292
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Hardcover. Condition: new. Hardcover. 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 This book provides a broad survey of both standard and nonstandard regression. Topics include those typically covered in a Master's-level statistics regression course- simple/multiple linear regression, residual analysis, ANOVA and specialized topics like biased regression procedures, multivariate regression, and count regression models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781498775298
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Handbook of Regression Methods
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