EPM5009 Categorical data and generalised linear models
Faculty of Medicine, Nursing and Health Sciences
EPM5009 Categorical data and generalised linear models is a level 5, 6-credit-point, postgraduate unit from the Faculty of Medicine, Nursing and Health Sciences, offered in 2022 in Semester 2 at Alfred Hospital. It needs MPH5040, EPM5003, EPM5014 and EPM5002 and unlocks 4 units.
- Credit points
- 6
- Offered in 2022
- Semester 2
- Alfred Hospital
- Assessment
- No exam
- 3 tasks
This is the 2022 handbook entry. See the 2023 entry.
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Requisites
Before EPM5009
Prerequisites
Pass these before you enrol.
Corequisites
Pass these before, or take them in the same semester.
After EPM5009
4 units list EPM5009 as a prerequisite or corequisite.
Enrolment rules
Overview
This unit will explore biostatistical applications of generalised linear models with an emphasis on underlying theoretical issues, and practical interpretation of the results of fitting these models. Relevant methods for 2 x 2 and 2 x k tables extended into logistic regression for a binary outcome as a special case of generalised linear modelling. Measures of association and modelling techniques for ordinal outcomes. Methods for analysing count data. Techniques for dealing with matched data e.g. from case control studies.
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Alfred Hospital | De |
Assessment
- Written assignment 135%
- Written assignment 235%
- Written assignment 330%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Understand the major theoretical aspects of generalised linear models.
- 2
Appreciate regression modelling strategies for generalised linear models.
- 3
Including estimation issues, choice of models, prediction and goodness of fit of a selected model.
- 4
Be proficient in the analysis of binary outcome data, either form a standard study design or from a matched study design.
- 5
Be capable of analysing ordered and unordered categorical outcomes using simple measures of association and complex regression models.
- 6
Be capable of analysing count data whether it satisfies standard distributional assumptions or whether it is over dispersed.
Learning resources
Required resources
See Moodle for details about required resources.
Contacts
- Chief Examiners
- Professor Andrew Forbes
- Unit Coordinators
- Dr Mark Jones
Common questions
What are the prerequisites for EPM5009?
You need MPH5040, EPM5003, EPM5014 and EPM5002 before you enrol; and EPM5004 before or alongside it. Enrolment rules also apply.
What can I take after EPM5009?
EPM5009 is a prerequisite or corequisite for 4 units, including EPM5008, EPM5011, EPM5013 and EPM5015.
When is EPM5009 offered?
In 2022, EPM5009 runs in Semester 2 at Alfred Hospital.
Does EPM5009 have an exam?
No. EPM5009 has 3 assessment tasks and no exam.