Regression Diagnostics: An Introduction

Description

Regression diagnostics are methods for determining whether a regression model that has been fit to data adequately represents the structure of the data. For example, if the model assumes a linear (straight-line) relationship between the response and an explanatory variable, is the assumption of linearity warranted? Regression diagnostics not only reveal deficiencies in a regression model that has been fit to data but in many instances may suggest how the model can be improved. The Second Edition of this bestselling volume by John Fox considers two important classes of regression models: the normal linear regression model (LM), in which the response variable is quantitative and assumed to have a normal distribution conditional on the values of the explanatory variables; and generalized linear models (GLMs) in which the conditional distribution of the response variable is a member of an exponential family. R code and data sets for examples within the text can be found on an accompanying website at https://tinyurl.com/RegDiag.  Half Title Series Publisher Note Acknowledgements Title Page Copyright Page CONTENTS Contributors Series Acknowledgements Chapter 1. Introduction Chapter 2. The Linear Regression Model: Review Chapter 3. Examining and Transforming Regression Data Chapter 4. Unusual Data: Outliers, Leverage, and Influence Chapter 5. Nonnormality and Nonconstant Error Variance Chapter 6. Nonlinearity Chapter 7. Collinearity Chapter 8. Diagnostics for Generalized Linear Models Chapter 9. Concluding Remarks References Index