STAT319-23S1 (C) Semester One 2023

Generalised Linear and Multivariate Models

15 points

Start Date: Monday, 20 February 2023
End Date: Sunday, 25 June 2023
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 5 March 2023
  • Without academic penalty (including no fee refund): Sunday, 14 May 2023


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 2023

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Wednesday 10:00 - 11:00 A9 Lecture Theatre
20 Feb - 2 Apr
24 Apr - 4 Jun
Lecture B
Activity Day Time Location Weeks
01 Friday 10:00 - 11:00 John Britten 117
20 Feb - 2 Apr
24 Apr - 4 Jun
Tutorial A
Activity Day Time Location Weeks
01 Monday 15:00 - 16:00 Rehua 008 Computer Lab
20 Feb - 2 Apr
24 Apr - 4 Jun

Course Coordinator

Daniel Gerhard


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


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

Note: 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.

Indicative Fees

Domestic fee $824.00

International fee $4,750.00

* 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

  • STAT319-23S1 (C) Semester One 2023