Categorical Data Analysis

• All course notes and assignments are available electorically from this website. We aim to make the notes self-sufficient, but it is difficult to convey the same amount of information compared to in-class teaching.
• You are encouraged to submit your reports electorically.
• All assignments can be completed at home, except the in-class midterm exam. A make-up exam would be provided for those who cannot make it due to H1N1, but let's hope that is not the case.
• Let the instructors know if you have any other concerns re. the potential impact of H1N1 on your learning.

General Information

• Time: Wednesdays, 10am - 1pm (We start at 10am sharp. Please arrive on time).
  
• Location: HSB 790.
  HSB: Health Sciences Building, 155 College Street (South on College and West of University).
  
  

Instructor

• Lei Sun (sun@utstat.toronto.edu)
  
• Laurent Briollais (laurent@mshri.on.ca)
  
  

Office Hour

• Wednesday noon/12:30-1pm (the last hour or half hour of the class).
  
  

Prerequisites and Enrollment

• This is a graduate course with the following prerequisites.
  
  • Statistics at the graduate level or consent of instructor.
  • Working knowledge of SAS/R or other equivalent software packages is necessary.
    
    
• All participating students must register! This is a graduate school policy.
  
  • October 5: The final date to enroll the course.
    
  • November 2: The final date to withdraw from the course.

Course Information

• Teaching objectives
  This course covers the fundamental statistical methods for analyzing categorical data, including logistic, poisson, and log-linear regression models; methods for goodness-of-fit, 2-by-2 tables, and stratified 2-by- 2 tables; maximum likelihood theory for generalized linear models; unconditional and conditional likelihood logistic regression; poisson regression; analysis of multi- dimensional contingency tables and log-linear models; comparison and contrast of different methods; model specification - choosing and assessing models; GLM; GEE.
  
  
• Format of instruction
  Lecutures.
  
  
• Evaluation
  Student evaluation will be based on 3 homework problem sets (30%), midterm (30%), term project (30%) and overall participation (10%).
  
  
• Text book
  Alan Agresti (2002). Categorical Data Analysis. Second edition. Wiley.

Course Outline (tentative, to be modified) Notes and Homework Assignments in PDF file

• September 16: session 1 (Lei Sun).
  Introduction - key distributions for categorical variables; a brief summary of classical statistical inference.
  
  
• September 23: session 2 (Lei Sun).
  Contigency Tables - hypthesis testing, CI, comparing two proportions, Homework 1
  
  
• September 30: session 3 (Lei Sun).
  Contigency Tables - OR, Small sample inference, SAS
  
  
• October 7: session 4 (Lei Sun).
  Logistic Regression - single predictor, Homework 2
  Tutorial - type 1 error, power and sample size
  
  
• October 14: session 5 (Lei Sun).
  Logistic Regression - multiple predictors
  
  
• October 21: midterm exam (10am-noon only; a single page two-sided notes is allowed; a calculator can be helpful).
  
  
• October 28: session 6 (Lei Sun).
  Logistic Regression and Poisson Regression
  
  
• November 4: session 7 (Lei Sun).
  OR inference from stratified samples; Models for matched pairs. Homework 3
  
  
• November 11: session 8 (Laurent Briollais).
  Introduction - Analyzing repeated categorical response data.
  
  
• November 18: session 9 (Laurent Briollais).
  GEE.
  
  
• November 25: session 10 (Laurent Briollais).
  Random effects.
  
  
• December 2: session 11 (Laurent Briollais).
  Mxiture models.
  
  
• December 9: term project.