STAT446-25S2 (C) Semester Two 2025

Advanced Generalised Linear and Multivariate Models

15 points

Details:
Start Date: Monday, 14 July 2025
End Date: Sunday, 9 November 2025
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 27 July 2025
  • Without academic penalty (including no fee refund): Sunday, 28 September 2025

Description

This course covers the statistical principles, data analysis techniques, the software analysis methods, and implementation in R, for Generalised Linear Models (GLM) and Multivariate Models.

STAT446 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.

Prerequisites

Subject to approval of the Head of School.

Timetable 2025

Students must attend one activity from each section.

Lecture A A
Activity Day Time Location Weeks
01 Monday 10:00 - 11:00 Rehua 002 Lectorial
14 Jul - 24 Aug
8 Sep - 19 Oct
Lecture B B
Activity Day Time Location Weeks
01 Tuesday 10:00 - 11:00 Psychology - Sociology 252 Lecture Theatre
14 Jul - 24 Aug
8 Sep - 19 Oct
Computer Lab A A
Activity Day Time Location Weeks
01 Wednesday 10:00 - 11:00 Jack Erskine 442 Computer Lab
14 Jul - 24 Aug
8 Sep - 19 Oct

Course Coordinator

Daniel Gerhard

Assessment

Assessment Due Date Percentage 
Test 30%
Assignment 10%
Project 10%
Final Examination 50%


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.

Notes

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 $1,138.00

* 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 STAT446 Occurrences

  • STAT446-25S2 (C) Semester Two 2025
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