Linear Models (LMR)

To enable students to apply methods based on linear models to biostatistical data analysis, with proper attention to underlying assumptions and a major emphasis on the practical interpretation and communication of results.


Semester 1: Dr Timothy Schlub, Sydney School of Public Health, University of Sydney

Semester 2: A/Prof Stephane Heritier, Prof Andrew Forbes, Dept of Epidemiology & Preventive Medicine, Monash University

Dr Timothy Schlub University of Sydney, Sydney School of Public Health Semester 1
A/Prof Stephane Heritier Monash University, Department of Epidemiology and Preventive Medicine Semester 2
General outline


Epidemiology, Mathematical Background for Biostatistics, Probability and Distribution Theory


Principles of Statistical Inference

Time commitment

8-12 hours total study time per week

Semester availability

Semester 1 & 2


Three assignments worth 35%, 25% and 40%

Prescribed Texts

No compulsory textbook

Special Computer Requirements

Stata or R statistical software


The method of least squares; regression models and related statistical inference; flexible nonparametric regression; analysis of covariance to adjust for confounding; multiple regression with matrix algebra; model construction and interpretation (use of dummy variables, parametrisation, interaction and transformations); model checking and diagnostics; regression to the mean;  handling of baseline values; the analysis of variance; variance components and random effects.

Special Computer Requirements

Course notes, assignment material and interaction facilities available online

NOTE: LMR is an important foundation unit. Students who do not develop a strong grasp of this material will struggle to become successful biostatisticians.