Units / EPM5027
EPM5027 · Regression modelling for biostatistics 1
2026 Handbook6 credit pointsLevel 5Department of Epidemiology and Preventive Medicine
Last checked: 23 Aug 2026 UTCOverview
This unit lays the foundation of biostatistical modelling to analyse data from randomised or observational studies. These skills are essential for biostatistics in practice and will be used by students for the remainder of their BCA studies. This unit will introduce the motivation for different regression analyses and how to choose an appropriate modelling strategy. This unit will teach how to use linear regression to analyse continuous outcomes and logistic regression for binary outcomes. Emphasis will be placed on interpretation of results and checking the model assumptions. Stata and R software will be used to apply the methods to real study datasets.
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Alfred Hospital | First semester | Teaching is all online (ONLINE) |
| Alfred Hospital | Second semester | Teaching is all online (ONLINE) |
Assessment
The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Written mathematical and analytical tasks (Equivalent to 1,800 words) | Written | 30% | — |
| 2 | Written modelling report (Equivalent to 1,800 words) | Written | 30% | — |
| 3 | Written analytical and modelling tasks (Equivalent to 2,400 words) | Written | 40% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
The Handbook lists no prerequisite, corequisite or prohibition for this unit.
Learning outcomes
- Explain the different research aims for which regression modelling can be applied.
- Select and apply a suitable regression modelling approach based on the specific research aim.
- Analyse data using normal linear models, and be able to assess model fit and diagnostics.
- Analyse data using logistic regression models for binary data, and be able to assess model fit and diagnostics.
- Accurately interpret and manipulate mathematical equations that relate to regression analysis.
- Communicate the outcomes and justification of a regression analysis.
Workload
Off campus: Twelve hours per week, consisting of (on average) 4 hours per week for reading core material, 4 hours per week completing exercises (manual, computer-based, or on-line), 2 hours per week for on-line communication via online moderated discussions, and 2 hours per week for assignment preparation. No residential component is required.
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