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Regression Diagnostics: An Introduction 2ed

SAGE Publications, Inc
Pub Date:
01/2020
ISBN:
9781544375229
Format:
Pbk 160 pages
Price:
AU$39.99 NZ$40.86
Product Status: Not Yet Published - See Pub Date for expected date
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 for examples within the text can be found on an accompanying website.

Series Editors Introduction
Acknowledgements
Chapter 1. Introduction
Chapter 2. The Linear Regression Model: Review
The Normal Linear Regression Models
Least-Squares Estimation
Statistical Inference for Regression Coefficients
The Linear Regression Model in Matrix Forms
Chapter 3. Examining and Transforming Regression Data
Univariate Displays
Transformations for Symmetry
Transformations for Linearity
Transforming Nonconstant Variation
Interpreting Results When Variables are Transformed
Chapter 4. Unusual data
Measuring Leverage: Hatvalues
Detecting Outliers: Studentized Residuals
Measuring Influence: Cook's Distance and Other Case-Deletion Diagnostics
Numerical Cutoffs for Noteworthy Case Diagnostics
Unusual Data: Details
Chapter 5. Non-Normality and Nonconstant Error Variance
Detecting and Correcting Non-Normality
Detecting and Dealing With Nonconstant Error Variance
Robust Coefficient Standard Errors
Bootstrapping
Weighted Least Squares
Robust Standard Errors and Weighted Least Squares: Details
Chapter 6. Nonlinearity
Component-Plus-Residual Plots
Marginal Model Plots
Testing for Nonlinearity
Modeling Nonlinear Relationships with Regression Splines
Chapter 7. Collinearity
Collinearity and Variance Inflation
Visualizing Collinearity
Generalized Variance Inflation
Dealing With Collinearity
*Collinearity: Some Details
Chapter 8. Diagnostics for Generalized Linear Models
Generalized Linear Models: Review
Detecting Unusual Data in GLMs
Nonlinearity Diagnostics for GLMs
Diagnosing Collinearity in GLMs
Quasi-Likelihood Estimation of GLMs
*GLMs: Further Background
Chapter 9. Concluding Remarks