BIOS6070
10 units
6000 level
Course handbook
Description
Introduces the core theoretical concepts and practical application issues relating to the most widely used analysis technique in contemporary health related research and linear regression modelling.
Availability2024 Course Timetables
Online
- Semester 1 - 2024
Learning outcomes
On successful completion of the course students will be able to:
1. Express linear regression models mathematically and construct mathematical arguments about regression concepts;
2. List and describe the assumptions of linear regression;
3. Use statistical software (such as SAS or Stata) to fit appropriate linear regression models answering specific questions;
4. Interpret and describe the results of regression models using non-technical language that clinicians or other clients can understand;
5. Use appropriate diagnostic tools to evaluate the reliability of fitted regression models;
6. Apply model building strategies which take into account interactions and confounders where applicable;
7. Recognise situations in which an analysis of covariance (ANCOVA) model should be applied, and implement this regression model using statistical software.
Content
Students will learn the fundamental theory behind linear modelling and how this is applied in health related 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.
Assessment items
Written Assignment: Assignment 1
Written Assignment: Assignment 2
Written Assignment: Assignment 3
Contact hours
Semester 1 - 2024 - Online
Self-Directed Learning-1
- Self-Directed 10 hour(s) per week(s) for 13 week(s) starting in week 1
- Suggest 8-12 hours time commitment per week (guide only).
Course outline
- BIOS6070 - Semester 1, 2024 (Online) (PDF, 186.3 KB)
The University of Newcastle acknowledges the traditional custodians of the lands within our footprint areas: Awabakal, Darkinjung, Biripai, Worimi, Wonnarua, and Eora Nations. We also pay respect to the wisdom of our Elders past and present.