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| == Session 0: Basics == Understanding of this material will be assumed throughout the course. Please read these introductory materials and make sure you understand them before beginning the readings for the first session. |
#acl HlpLabGroup:read,write,delete,revert All:read #format wiki #language en #pragma section-numbers 4 |
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| If there is sufficient interest, a short primer on using R will be offered during the first week of the course (somewhere between May 26 and May 30). If you're interested in this, or if you have requests on what to cover during the primer, please write to AustinFrank. | == Session 1: Linear regression == '''May 29 2008''' This session will cover the basics of linear regression. See below for a [#Topics list of topics]. |
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| || Baa08 || Chapter 1 (pp. 1-20) || Intro to R. || || G&H07 || Chapter 2 (pp. 13-26) || Intro to probability theory.|| || Dal04 || ??? || ??? || |
|| G&H07 || Chapter 3 (pp. 29-49) || Linear regression: the basics || || Baa08 || Section 4.3.2 (pp. 91 - 105) || Functional relations: linear regression || || || Sections 6 - 6.2.1 (pp. 181-198) || Regression Modeling (Introduction and Ordinary Least Squares Regression) || || || 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. == [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 |
|
Sections 6 - 6.2.1 (pp. 181-198) |
Regression Modeling (Introduction and Ordinary Least Squares Regression) |
|
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.
[Anchor(Topics)] Topics