Regression Modelling for Biostatistics 1 (RM1)

To 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.

Armando Teixeira-Pinto portrait
Prof Armando Teixeira-Pinto University of Sydney, Sydney School of Public Health Semester 1
Stephane Heritier portrait
Prof Stephane Heritier Monash University, Department of Epidemiology and Preventive Medicine Semester 2
General outline


Epidemiology, Mathematical Foundations for Biostatistics


Principles of Statistical Inference

Time commitment

8-12 hours total study time per week

Semester availability

Semester 1 & 2


Three assignments worth 30%, 30% and 40%.

Prescribed Texts

Vittinghoff E, Glidden D, Shiboski S, McCulloch C. Regression Methods in Biostatistics: Linear, logistic, survival and repeated measures models. 2nd Edition. Springer Verlag 2012

Special Computer Requirements

Stata or R statistical software


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


Course notes, online mini-lecture videos, online tutorials, discussion board


Co-requisites may be taken before or concurrently.

Program coordinator approval is required for taking RM1 & EPI simultaneously. 

The BCA acknowledges we live and work on the ancestral lands of Aboriginal and Torres Strait Islander peoples, who have for thousands of generations exchanged knowledge for the benefit of all. We pay our respects to those who have cared and continue to care for Country.