Linear Regression Modelling


Introduces the core theoretical concepts and practical application issues of the most widely used analysis technique in modern health related research, linear regression modelling.


Distance Education - Callaghan

  • Semester 2 - 2016
  • Semester 1 - 2017
  • Semester 2 - 2017

Learning Outcomes

On successful completion of the course students will be able to:

1. Understand the theoretical and practical issues involved in linear regression modelling;

2. Correctly fit, interpret and diagnose regression models using statistical software (SAS);

3. Utilise model building strategies which take into account interactions and confounders where applicable;

4. Understand the analysis of covariance (ANCOVA) model and the situations when it should be applied;

5. Interpret and describe the results of a regression model in a manner which clinicians or other clients can understand.


Students will learn the fundamental theory behind linear modelling and how this is applied in medical research. The importance of correct specification of the regression model and other model assumptions are explained, along with diagnostic tools for assessing how well the model fits the data. Multiple linear regression is then introduced along with the concepts of confounding, interaction and model building. Correct inference of regression parameters is emphasised throughout the course.





Must be enrolled in Graduate Diploma of Medical Biostatistics or Master of Medical Statistics to enrol in this course. Pre-requisites: must have successfully completed BIOS6040, BIOS6170, and EPID6420.

Assessment Items

Written Assignment: Essays / Written Assignments

Quiz: Short assignments plus online quizs