Spring 2021 - STAT 851 G100

Generalized Linear Models and Discrete Data Analysis (4)

Class Number: 3346

Delivery Method: In Person

Overview

  • Course Times + Location:

    Tu, Th 12:30 PM – 2:20 PM
    REMOTE LEARNING, Burnaby

  • Prerequisites:

    STAT 830 and STAT 850 or permission of instructor.

Description

CALENDAR DESCRIPTION:

The theory and application of statistical methodology for analyzing non-normal responses. Special emphasis on contingency tables, logistic regression, and log-linear models. Other topics can include mixed-effects models and models for overdispersed data.

COURSE DETAILS:


Course Outline:

1. Analysis of contingency tables
2. Generalized linear models (GLMs)
   a. The exponential family
   b. Link functions
   c. Relationship to linear models
   d. Iterated reweighted least-squares estimation
3. Models for overdispersed data
   a. Quasi-likelihood
   b. Introduction to estimating functions
4. Models for multinomial data
   a. Log-linear models
   b. Ordinal  responses

Mode of Teaching

  • Lecture: Synchronous (recorded)

Grading

  • Assignments 40%
  • Midterm 40%
  • Final Project 20%

NOTES:

Above grading is subject to change.

Materials

RECOMMENDED READING:

Categorical Data Analysis, 3rd ed. Author: Alan Agresti. Publisher: Wiley
ISBN: 978047046365

Generalized Linear Models, 2nd ed. Authors: McCullagh and Nelder. Publisher: CRC Press
ISBN: 9780412317606

An Introduction to Generalized Linear Models, 3rd ed. Authors: Dobson and Barnett. Publisher: CRC Press
ISBN: 9781584889502

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN SPRING 2021

Teaching at SFU in spring 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).