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| This session will cover the basics of linear regression. See below for a [#Topics list of topics]. |
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=== Notes on the readings === === Additional terminology === Feel free to add terms you want clarified in class: * * === Questions === * Q: |
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== Suggested topics == If you have any material that you would like to cover that isn't included in the list below, please make note of it here. [[Anchor(Topics)]] == Topics == === Interacting with R and R files === * Using a command line command history, continuation lines, stopping execution [[BR]] defining variables [[BR]] calling functions * Installing packages {{{install.package(), update.package()}}} * Using the R workspace {{{ls(), rm(), setwd(), getwd(), library()}}} * Using an R script file * Saving R objects {{{save(), save.image()}}} |
Session 1: Linear regression
May 29 2008
This session will cover the basics of linear regression. See below for a [#Topics list of topics].
Reading
G&H07 |
Chapter 3 (pp. 29-49) |
Linear regression: the basics |
Baa08 |
Section 4.3.2 (pp. 91 - 105) |
Functional relations: linear regression |
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Sections 6 - 6.2.1 (pp. 181-198) |
Regression Modeling (Introduction and Ordinary Least Squares Regression) |
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Section 6.6 (pp. 258-259) |
General considerations |
Notes on the readings
Additional terminology
Feel free to add terms you want clarified in class:
Questions
- Q:
Assignments
Send your solutions to Andrew Watts, who will upload them here. Please send them by Friday 3:30pm.
G&H07 |
Section 3.9 (pp. 50-51) |
Exercises 3 and 5 |
Baa08 |
Section 4.7 (p. 126) |
Exercises 3 and 7* |
* (for Exercise 7, Baayen treats linear regression using lm or ols as the same as analysis of covariance (see section 4.4.1 (pp. 117-119))).
Suggested topics
If you have any material that you would like to cover that isn't included in the list below, please make note of it here.
Topics