Categorical Data Analysis

General Information

• Time: Mondays, 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

• Mondays noon/12:30-1pm (the last hour or half hour of the class).
  
• There is no TA for the course.
  
  

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.
  
  • September 26: The final date to enroll the course.
    
  • October 31: 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 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 12: session 1 (Lei Sun).
  Introduction - key distributions for categorical variables; a brief summary of classical statistical inference, hw1
  
  
• September 19: session 2 (Lei Sun).
  Contigency Tables - hypthesis testing, CI, comparing two proportions
  
  
• September 26: session 3 (Lei Sun).
  Contigency Tables - OR, Small sample inference, SAS
  Tutorial - review hw1 (please hand in hw1 on time) and distributions of various tests
  
  
• October 3: session 4 (Lei Sun).
  Logistic Regression - single predictor
  Tutorial - type 1 error, power and sample size
  
  
• October 10: no class; university closed for Thanksgiving Day.
  
  
• October 17: session 5 (Lei Sun).
  Logistic Regression - multiple predictors
  Tutorial - review hw2 (please hand in hw2 on time)
  
  
• October 24: session 6 (Lei Sun).
  Logistic Regression and Poisson Regression
  Tutorial - review hw3 (please hand in hw3 on time)
  
  
• October 31: session 7 (Lei Sun).
  OR inference from stratified samples; Models for matched pairs.
  Tutorial - review hw4 (please hand in hw4 on time)
  
  
• November 7: midterm exam (10am-noon only; a single page two-sided notes is allowed; a calculator can be helpful).
  
  
• November 14: session 8 (Laurent Briollais).
  Logit models for nominal and ordinal responses and introduction to methods for repeated categorical data.
  
  
• November 21: session 9 (Laurent Briollais).
  GLM, Quasi-likelihood and GEE for repeated categorical data.
  
  
• November 28: session 10 (Laurent Briollais).
  Random effects models for repeated categorical data.
  
  
• December 5: session 11 (Laurent Briollais).
  Transitional models for repeated categorical data and case study.
  
  
• December 16: Term project due.