Regression Diagnostic Series: Qualitative applications in the social sciences / John Fox

Table of Contents

I. Introduction
2. Linear least-squares regression
The regression model
Least-squares estimation
Statistical inference for regression coefficients
The general linear model

3. Collinearity
Collinearity and variation inflation Coping with Collinearity: No quick fix

4. Outfying and Influential data
Measuring leverage: hat-values
Detecting outliers: studentized residuals
Measuring influence: Cook's distance and other diagnostics
Numerical cutoffs for diagnostics statistics subsets of observation: partial-regression
Should unusual data be discarded

5. Non-distributed errors
Normal quintile-comparison plot of residuals
Histograms of residuals
Correcting asymmetry by transformation

6. Nonconstant error variation
Detecting Nonconstant error variance
Correcting Nonconstant error variance

7. Nonlinearity
Residual and partial-residual plots
Transformations for linearity

8 Discrete data
Testing for nonlinearity
Testing for nonconstant error variance

9. Maximum-likelihood methods, score tests, and constructed variables
Box-cox transformation of y
Box-Tidwell transformation of the xs
Nonconstant error variance revised

10. Recommendations
Computing diagnostics
Further reading