Week Dates Topics Reading Due
1 Jan 13-17

Tue

  • Introductions and Syllabus
  • The Challenger Disaster Case study:
    NBC Video and R Rmd and html

Thur

  • Binomial and multinomial distributions
  • Inference for one proportion
    (exact binomial or approximate normal) - Rmd and html
  • Maximum likelihood estimation - R code
 Ch. 1 Sections 1.1-1.3  
2 Jan 20-24

Tue

  • Probability structures in contingency tables
  • Sampling methods
  • Types of studies
  • Odds ratios and relative risk

Thur

  • Inference for contingency tables:
    randomization tests, Fisher's exact test - Rmd and html
 Ch. 2 Sections 2.1-2.3, 2.6.1-2.6.2 HW 1 due Thur 1/23
3 Jan 27-31

Prof. Hancock will be at a conference this week.
Watch the video lectures in D2L, and complete the
accompanying practice worksheets either on your own or with your classmates during class time.

Tue/Thur

  • Inference for contingency tables: large-sample chi-squared tests - Rmd and html
  • Three-way contingency tables and Simpson's Paradox
 Ch. 2 Sections 2.4, 2.7  
4 Feb 3-7

Tue

  • Finish randomization tests and three-way tables - R code
  • Components of a generalized linear model (GLM)

Thur

  • Fitting and interpreting GLMs for binary response
  • Inference and model fitting for GLMs with binary response

  • Simple logistic regression models and inference

  • Framingham example - Rmd and html (updated 2/13)
Ch. 3 Section 3.1-3.2;
Ch. 4 Sections 4.1-4.2 		HW 2 due Thur 2/6
5 Feb 10-14

Tue

  • Summarizing predictive power in logistic regression using classification tables and ROC curves (see Framingham example)

Thur

  • Multiple logistic regression (see Framingham example)

Ch. 4 Sections 4.3-4.6

 Quiz 1 Thur 2/13: 
Chapters 1-2
(excluding Sections 1.4, 1.5,
2.5, and 2.6.3-2.6.6)
6 Feb 17-21

Tue

  • Logistic regression with categorical predictors - R code (updated Feb 20)

Thur

  • Linear contrasts (not in textbook) - R code (updated Feb 25)
Ch. 4 Sections 4.3-4.5

 HW 3 due Thur 2/20
7

Feb 24-28

Tue

  • GLMs for counts: Poisson regression - R code

Thur

  • Poisson regression (cont)
 Ch. 3 Section 3.3 Quiz 2 Thur 2/27: Chapters 3-4 (excluding Sections 3.3-3.5)
8 Mar 2-6

Tue/Thur

  • Poisson models with rates - R code (updated Mar 5)
  • Overdispersion in Poisson regression and goodness-of-fit using deviance
 (cont) HW 4 due Thur 3/5
9 Mar 9-13

Tue/Thur

  • Model fitting, checking, and selection for generalized linear models
  • Hosmer-Lemeshow goodness-of-fit test for a binary response - binary.gof R function
  • Infinite estimates in logistic regression
  • Zero-inflated Poisson models (if time)
 Ch 5 Sec. 5.1-5.3 Quiz 3 Thur 3/12
-- Mar 16-20 Spring Break    
10 Mar 23-27

Tue

  • No class

Thur

  • Introduction to online instruction
  • Model selection practice - Rmd and html
  

Project data and research questions due Fri 3/27
11 Mar 30 - Apr 3

Tue/Thur

 Ch. 6 Sections 6.1-6.2

HW 5 due Thur 4/2

Project proposal due Fri 4/3
12 Apr 6-10

Tue/Thur

  • Paired t-tests (intro stat review) - R code
  • McNemar's test for matched binary data - Rmd and html
 Ch. 8 Sections 8.1-8.2

 Take a day off and enjoy the nice weather!
13 Apr 13-17

Tue/Thur

  • Marginal models and generalized estimating equations
  • Generalized linear mixed models
  • Epilepsy example - R code (updated Apr 23)
Ch. 9 Sections 9.1-9.2;
Ch. 10 Sections 10.1-10.2

Quiz 4 Tue 4/14

Project report draft due Fri 4/17
14 Apr 20-24

(continued)

  • Abortion attitudes example - R code
  

HW 6 due Thur 4/23

Project peer assessments due Fri 4/24
15 Apr 27 - May 1

Tue:  Optional Zoom review session

Thur:  Project presentations

  

Quiz 5 Tue 4/28

Project presentation slides due Wed 4/29

Project report and group evaluation due Fri 5/1
Finals May 4-8

Take-home comprehensive final exam:
Due Monday, May 4, 5:00pm