STAT446-23S1 (C) Semester One 2023

Advanced Generalised Linear and Multivariate Models

This occurrence is not offered in 2023

15 points

Details:
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

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.

Course Coordinator

Daniel Gerhard

Assessment

Assessment Due Date Percentage  Description
Assignments 10%
Exam 50%
Project 10% Project with written report and oral presentation
Test 30%

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

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 $1,045.00

* 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

  • STAT446-23S1 (C) Semester One 2023 - Not Offered
  • STAT446-23S1 (D) Semester One 2023 (Distance) - Not Offered