Applied Regression Analysis and Generalized Linear Models, Second Edition

Data-Analysis Exercises

Chapter Exercises
2 What is regression analysis? Chapter-2-Exercises.pdf
3 Examining data Chapter-3-Exercises.pdf
4 Transforming data Chapter-4-Exercises.pdf
5 Linear least-squares regression Chapter-5-Exercises.pdf
6 Statistical inference for regression Chapter-6-Exercises.pdf
7 Dummy-variable regression Chapter-7-Exercises.pdf
8 Analysis of variance Chapter-8-Exercises.pdf
9 Statistical theory for linear models Chapter-9-Exercises.pdf
10 The vector geometry of linear models (No data-analysis exercises)
11 Unusual and influential data Chapter-11-Exercises.pdf
12 Normality, constant variance, and linearity Chapter-12-Exercises.pdf
13 Collinearity and its purported remedies Chapter-13-Exercises.pdf
14 Logit and probit models Chapter-14-Exercises.pdf
15 Generalized linear models Chapter-15-Exercises.pdf
16 Time-series regression Chapter-16-Exercises.pdf
17 Nonlinear regression Chapter-17-Exercises.pdf
18 Nonparametric regression Chapter-18-Exercises.pdf
19 Robust regression Chapter-19-Exercises.pdf
20 Missing data in regression models Chapter-20-Exercises.pdf
21 Bootstrapping regression models Chapter-21-Exercises.pdf
22 Model selection, averaging, and validation Chapter-22-Exercises.pdf

Note: As a general matter, you should feel free to substitute appropriate data sets of interest to you for those suggested in the various data-analysis exercises.

You can also download a zip file with all of the exercises.

Last Modified: 16 February 2011 by John Fox <jfox AT mcmaster dot ca>