1

1/9

review

review prerequisite topics

1. guide to studying for a quantitative course
2. master list of practice problems
3. one page of practice problems from a past nsees diagnostic exam
4. review of t-test and anova: limpet experiment and solution sketches

2

1/14

1/16

review of scope of inference, t-tests, anova,simple linear regression

readings for lectures

overview of regression: m&m, sections 2.1-2.3

review of one-way anova

lab agenda

simple linear regression

mon. 1/13: review of prereq. topics. room tba, 6-7pm, bring questions

thurs. 1/16, quiz 1 on prerequisite materials, 25 mins, closed book, bring scientific calculator.

3

1/21

1/23

simple linear regression

ch. 7 of sleuth

m&m, sections 2.1-2.3 as well as chapter 10.

lab agenda

in-class quiz (not for credit) (1/23) with solutions and review materials

add/drop ends january 22nd.

4

1/28

1/30

assumptions for simple linear regression

ch 7 of sleuth

m&m, section 10.2

by now you can do all linear regression practice problems.

wa1 assigned 1/30

5

2/4

2/6

assumptions for simple linear regression

ch 8 of sleuth

m&m, section 10.2

1. topics: regression effect, simultaneous inference, anova table for regression, lack of fit test

2. Lab agenda

3. Check this out: an applet demonstrating how certain (X,Y) pairs may influence the regression line.

1. WA1 due at 12:40pm 2/6 in class.
2. Fill out on-line course evaluation.

6

2/11

2/13

Log transformations

Multiple Regression

Ch 8 of Sleuth

1. Summary of Feb. 11, 13 lectures.
2. Lab agenda for Weds. Feb 12
3. Handout on mammal brain weight data
4. Plotting regressions of log transformed data on their original scale in Splus

Weds Feb 12 quiz in lab, 20 minutes, closed book, bring scientific calculator

Week

Date

Topics

Reading

Handouts

Due Dates

7

2/18

2/20

Ch 9 of Sleuth

1. Handout on dichotomous indicator variables: example of red spruce forests in App. Mtns.
2. Handout of past one-page writeups of this example from Spring 2002 course. Class discussion and critique.

8

2/25

2/27

Regression with indicator variables

Inferential tools

Ch 9 of Sleuth

Ch 10 of Sleuth

M&M, Ch 11

1. Lab agenda: Practice using "indicator" or "dummy" variables: Meadowfoam data, 9.1.1
2. Handout of Splus commands for meadowfoam analysis (2/28/02 "aov.Splus.txt"), with ESS F-test info on back .

1. Writing assignment 2 due 5pm 3/7.

9

3/4

3/6

Inferential tools

Ch 10 of Sleuth

M&M, Ch 11

1. Handout on Bat Echolocation Dataset, Case study from Sleuth includes Science article, "No cost of echolocation for bats in flight"
2. Splus commands handout: a script to plot a log-log regression on its original scale (using pollen data); coding multiple-level dummy variables; centering trick; 3 ANOVA tables for 3 bat models.
3. Polynomial regression handout
4. Handout: Course Outline through Chapter 10
5. Lecture notes from 3/6

1. WA2 Due, 3/7, 5pm, no late papers accepted.

10

3/11

3/13

Spring Break

11

3/18

3/20

Case influence diagnostics

Strategies for variable selection

Chapter 11 of Sleuth

Ch 12 of Sleuth, also material on adjusted R2 from Ch10, as well as partial residual plots from Ch11.

1. Chapter 11 handouts: pollen data analysis, model checking and refinement through assessment of influence and leverage
2. Lab Agenda:Covers ESS F-tests, Diagnostics. Make sure you know how to paste figures so they can be emailed documents for future assignments, as well as the location of the Course Drop-Box.

1. Project Proposals will be accepted via e-mail to "Course Drop Box" found under "Communications" Project Proposal Final Deadline, Friday, 3/21, 5pm

12

3/25

3/27

Strategies for variable selection

Ch 12 of Sleuth

1. Quiz Tues 3/25
2. WA3 (take-home midterm) assigned 3/25, 8am and due 4/2, 5pm

13

4/1

4/3

Model Selection, continued. Comparisons of proportions or odds; Tools for tables of counts

MP Presentations 4/3: No class, but critiques will be assigned.

Ch 12

Lab: Optional due to MP Presentations

1. 4/2 WA3 due, 5pm, either to 223C Old Chem or email to instructor.
2. 4/3 no class

14

4/8

4/10

Model selection, validation, predictive power of models, Bayesian methods

1. Polyreg notes
2. Polynomial Regression 2
3. Lab Agenda
   1. Overview of Steps in Model Selection
   2. Model selection functions
      • select.cp.aic.bic.s
      • select.cp.aic.bic.keep.s
      • bicreg.s
      • bicreg.keep.s
      • A comparison of BIC, AIC, R2, R2adj as a function of # parameters and Splus script to create this plot.
      • A function to take main effects and create a dataset with main effects and all 2 way interactions
      • Plots influence diagnostics, prints out a dataframe of values. (revised 4/10)

15

4/15

4/17

Overview of logistic regression (binary response)

and relation to CART models

Required reading for logistic Regression with Solutions to Problems; Chapter 20 + conceptual exercises in Sleuth

Project Presentations, Thursday, April 17th, 4-8pm. Room A150

16

4/22

4/24

Reading Week

17

Week of 4/28

Final Exam: Self-scheduling, Monday & Tuesday 9-2pm; Wednesday 9-12, A247. .