At the completion of this course students should be able to:
1. understand the major theoretical and computational issues underlying analyses based on linear models
2. develop appropriate regression modelling strategies based on course matter considerations, including choice of models, control for confounding and appropriate parametrisation
3. be proficient at using a statistical software package (eg Stata) to perform multiple regression and analysis of variance
4. understand the construction, use and interpretation of regression modelling diagnostics
5. express the results of statistical analyses of linear models in language suitable for communication to medical investigators or publication in biomedical or epidemiological journal articles
6. appreciate the role of modern techniques including nonparametric smoothing and variance components models

By a series of case studies, students will explore extensions of methods for group comparisons of means (t-tests and analysis of variance) to adjust for confounding and to assess effect modification/interaction, together with the development of associated inference procedures. Multiple regression strategies and model selection issues will be presented together with model checking and diagnostics. Nonparametric regression techniques, and random effects and variance components models will be outlined as an introduction to a broader class of regression models.

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Epidemiology (EPID6420);*
Mathematical Background for Biostatistics (BIOS6040);
Principles of Statistical Inference (BIOS6050);
Probability and Distribution Theory (BIOS6170);
*Co-requisite. Please note, Program Coordinator approval is required for taking EPI and LMR simultaneously.

Distance Learning : Paper Based

Self Directed Learning

Self Directed Learning: for 6 hour(s) per Week for Full Term