Linear Models

Description

Explores biostatistical applications of linear models with an emphasis on underlying theoretical and computational issues, practical interpretation and communication of results.

This course is offered in conjunction with the Biostatistics Collaboration of Australia (BCA).

Availability

Distance Education - Callaghan

  • Semester 1 - 2015
  • Semester 2 - 2015

Learning Outcomes

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

Content

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.

Requisites

This course is only available to students enrolled in the Graduate Diploma in Medical Statistics or Master of Medical Statistics programs.

Assumed Knowledge

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.

Assessment Items

Written Assignment: Essays / Written Assignments

Quiz: Short assignments plus online quizs

Contact Hours

Self-Directed Learning

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

Suggest 8-12 hours time commitment per week (guide only).