STAT319-24S2 (C) Semester Two 2024

Generalised Linear and Multivariate Models

15 points

Start Date: Monday, 15 July 2024
End Date: Sunday, 10 November 2024
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 28 July 2024
  • Without academic penalty (including no fee refund): Sunday, 29 September 2024


STAT319 is a course in Generalised Linear Models (GLM), suited to anyone with an interest in analysing data. In this course we introduce the components of GLM and other advanced data analysis techniques. We use the free-ware package R. R is becoming the preferred computer package for many statisticians. In this course we will show you how to use the package, enter, manipulate and analyse data in R.

STAT319 is a course in generalised linear models (GLM), a very useful and frequently used class of models for practical data analysis. Additional to normally distributed responses, a GLM allows us to model count data (0, 1, 2, 3, ...), binary data (success/failure, alive/dead, pass/fail, etc.), or categorical data (A/B/C/D, never/sometimes/often, etc.).

Topics that are usually covered in the course include:
• Exponential families
• Link functions
• Binary regression models
• Modelling count data
• Iteratively re-weighted least-squares
• Likelihood inference / profile likelihood
• Modelling overdispersion
• Extensions to generalised linear mixed-effects models (GLMM)
• Time-to-event modelling

Learning Outcomes

Understand the concept of generalised linear models
Apply generalised linear (mixed-effects) models to count and categorical data
Adequately report and interpret model results

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attributes specified below:

Critically competent in a core academic discipline of their award

Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

Employable, innovative and enterprising

Students will develop key skills and attributes sought by employers that can be used in a range of applications.


30 points from STAT202-299

Timetable 2024

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Thursday 11:00 - 12:00 Psychology - Sociology 252 Lecture Theatre
15 Jul - 25 Aug
9 Sep - 20 Oct
Lecture B
Activity Day Time Location Weeks
01 Wednesday 11:00 - 12:00 Psychology - Sociology 252 Lecture Theatre
15 Jul - 25 Aug
9 Sep - 20 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Tuesday 13:00 - 14:00 Jack Erskine 442 Computer Lab
15 Jul - 25 Aug
9 Sep - 20 Oct

Examinations, Quizzes and Formal Tests

Test A
Activity Day Time Location Weeks
01 Wednesday 18:30 - 19:30 Jack Erskine 340
19 Aug - 25 Aug

Course Coordinator

Daniel Gerhard


Assessment Due Date Percentage 
Assignment 1 10%
Test 30%
Final Examination 60%

To pass this course, you must both pass the course as a whole (≥50% over all the assessment items) and obtain at least 40% in the final examination.


Course requirements
You should be familiar with
• the fundamentals of probabilistic modelling:
   ◦ probabilities
   ◦ distribution functions
   ◦ densities
   ◦ expected values
   ◦ likelihood functions
   ◦ estimators
   ◦ the central limit theorem
• Linear regression
• basic knowledge of using the statistical software R

Indicative Fees

Domestic fee $847.00

International fee $4,988.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics .

All STAT319 Occurrences

  • STAT319-24S2 (C) Semester Two 2024