Applied Regression Analysis, Linear Models, And Related Methods - 97 edition

Fox, John : McMaster University

"I have never read a book on regression that reflects as broad and profound a grasp of the concepts of statistics as this book does. In every topic John Fox deals with--and he does not avoid the slippery ones--he shows a clarity and depth of understanding that goes beyond anything else I have seen in textbooks and that matches the works of the leading researchers within each field."

--Georges Monette, Department of Mathematics and Statistics, York University

"The selection of examples throughout the book is one of its strengths, as they are generally quite engaging in 'real-world' interest, and demonstrate the practical use (and limitations) of the statistical methods far better than contrived data. I appreciate the fact that John Fox describes what each example 'means' in terms of the substantive problem behind the data--students would find this quite useful."

--Michael Friendly, Psychology Department, York University

Sage Publications Web Site, June, 2000

I. Preliminaries

1. Statistics and Social Science
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. Nonlinearity and Other Ills
13. Collinearity

IV. Beyond Linear Least Squares

14. Extending Linear Least Squares
15. Logit and Probit Models
16. Assessing Sampling Variation

Appendixes