Handbook of Regression Analysis
Samprit Chatterjee, Jeffrey S. Simonoff(auth.)A Comprehensive Account for Data Analysts of the Methods and Applications of Regression Analysis.
Written by two established experts in the field, the purpose of the Handbook of Regression Analysis is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of regression methods, but it has been deliberately written at an accessible level.
The handbook provides a quick and convenient reference or “refresher” on ideas and methods that are useful for the effective analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (including linear, binary logistic, multinomial logistic, count, and nonlinear regression models). Theory underlying the methodology is presented when it advances conceptual understanding and is always supplemented by hands-on examples.
References are supplied for readers wanting more detailed material on the topics discussed in the book. R code and data for all of the analyses described in the book are available via an author-maintained website.
Content:Chapter 1 Multiple Linear Regression (pages 1–21):
Chapter 2 Model Building (pages 23–49):
Chapter 3 Diagnostics for Unusual Observations (pages 51–65):
Chapter 4 Transformations and Linearizable Models (pages 67–79):
Chapter 5 Time Series Data and Autocorrelation (pages 81–109):
Chapter 6 Analysis of Variance (pages 111–137):
Chapter 7 Analysis of Covariance (pages 139–146):
Chapter 8 Logistic Regression (pages 147–176):
Chapter 9 Multinomial Regression (pages 177–190):
Chapter 10 Count Regression (pages 191–213):
Chapter 11 Nonlinear Regression (pages 215–225):
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