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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
Understand the concept of generalised linear modelsApply generalised linear (mixed-effects) models to count and categorical data Adequately report and interpret model results
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
Students must attend one activity from each section.
Course requirementsYou 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 RNote: 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.
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