Regression Modelling for Biostatistics I

Postgraduate | 2026

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Area/Catalogue
BIOL 5035
Course ID icon
Course ID
203061
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
5
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No
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Note:
Course data is interim and subject to change

Course overview

This course will enable students to apply methods based on linear and logistic regression models to biostatistical data analysis, with proper attention to underlying assumptions and a major emphasis on the practical interpretation and communication of results. Specifically, this course lays the foundation of biostatistical modelling to analyse data from randomised or observational studies, creating skills that are essential for biostatistics in practice and that will be used by students for the remainder of their BCA studies. This course will introduce the motivation for different regression analyses and how to choose an appropriate modelling strategy. 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.

Course learning outcomes

  • See Study Guides at: https://url.au.m.mimecastprotect.com/s/jyzMCmO5QMCjMl1XJtJi1URPVES?domain=bca.edu.au/
  • Explain the motivations for different regression analyses and be able to select and apply a suitable modelling approach based on the 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
  • Effectively communicate the outcomes and justification of a regression analysis

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A