|
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 |