Handbook of Regression Methods

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A01=Derek Scott Young
advanced regression techniques for researchers
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Age Group_Uncategorized
ANOVA
ANOVA Table
Author_Derek Scott Young
automatic-update
Carapace Length
Category1=Non-Fiction
Category=PBT
censored data
Cochrane Orcutt Procedure
COP=United States
data mining
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Deming Regression
Durbin Watson Test
eq_isMigrated=2
eq_nobargain
Exponential Smoothing
generalized linear models
Glejser's Test
Glejser’s Test
Instrumental Variables Regression
Language_English
linear models
Linearly Independent
Measurement Error Model
multicollinearity analysis
Multiple Linear Regression
Multiple Linear Regression Model
Pa Rti
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Partial Autocorrelations
Partial Regression Plot
Partial Residual Plot
Pr Ic
Prediction Intervals
Price_€100 and above
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Recursive Residuals
regression diagnostics
Regression Model
Residual Standard Error
robust estimation
semiparametric regression
Simple Linear Regression Fit
Simple Linear Regression Model
softlaunch
statistical modeling
Subsets Procedure
Tolerance Intervals

Product details

  • ISBN 9781498775298
  • Weight: 760g
  • Dimensions: 156 x 234mm
  • Publication Date: 05 Jul 2017
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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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

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.

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