Table of contents for Applied regression analysis and generalized linear models / John Fox.
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Preface
1 - Statistical Models and Social Science
I - DATA CRAFT
2 - What is Regression Analysis?
3 - Examining Data
4 - Transforming Data
II - LINEAR MODELS AND LEAST SQUARES
5 - Linear Least-Squares Regression
6 - Statistical Inference for Regression
7 - Dummy-Variable Regression
8 - Analysis of Variance
9 - Statistical Theory for Linear Models
10 - The Vector Geometry of Linear Models
III - LINEAR-MODEL DIAGNOSTICS
11 - Unusual and Influential Data
12 - Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity
13 - Collinearity and its Purported Remedies
IV - GENERALIZED LINEAR MODELS
14 - Logit and Probit Models
15 - Generalized Linear Models
V - EXTENDING LINEAR AND GENERALIZED LINEAR MODELS
16 - Time-Series Regression
17 - Nonlinear Regression
18 - Nonparametric Regression
19 - Robust Regression
20 - Missing Data in Regression Models
21 - Bootstrapping Regression Models
22 - Model Selection, Averaging, and Validation
A Notation
References
Library of Congress subject headings for this publication:
Regression analysis.
Linear models (Statistics)
Social sciences -- Statistical methods.