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| === Notes on the readings === === Additional terminology === Feel free to add terms you want clarified in class: * * === Questions === * Q: == 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(assignments)]] |
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[[Anchor(Topics)]] == Topics == * More on outliers * detect outliers {{{boxplot()}}}, scatterplots {{{plot(), identify()}}[[BR]] * dealing with outliers exclusion {{{subset()}}}[[BR]] robust regression (based on t-distribution): {{{tlm()}}} (in package {{{hatt}}}))[[BR]] * Overly influential cases (can be, but don't have to be outliers) {{{lm.influence()}}}, also {{{library(Rcmdr)}}}[[BR]] * Model evaluation: Where is the model off? * case-by-case: {{{residuals(), predict()}}}[[BR]] * based on predictor: {{{residuals()}}} against predictors, {{{calibrate()}}}[[BR]] * overall: {{{validate()))[[BR]] * Corrections: * correcting for clusters (violation of assumption of independence): {{{bootcov()}}}[[BR]] |
Session 2: Issues in linear regression
June 3 2008
Reading
G&H07 |
Chapter 4 (pp. 53-74) |
Linear regression: before and after fitting the model |
Baa08 |
Sections 6.2.2-6.2.4 (pp. 198-212) |
Collinearity, Model criticism, and Validation |
|
Section 6.4 (pp. 234-239) |
Regression with breakpoints |
Notes on the readings
Additional terminology
Feel free to add terms you want clarified in class:
Questions
- Q:
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.
Assignments
Send your solutions to Andrew Watts, who will upload them here. Please send them by Wednesday 3:30pm or upload them yourself to this page by 10pm.
G&H07 |
Section 4.9 (p.76) |
Exercise 4 |
Baa08 |
Section 6.7 (p. 260) |
Exercise 1, 8 |
In addition to the book problems, we will distribute a data set from the ongoing ngrams project.