Linear Models

Course code BIOS6070Units 10Level 6000Faculty of Health and MedicineSchool of Medicine and Public Health

Explores biostatistical applications of linear models with an emphasis on underlying theoretical and computational issues, practical interpretation and communication of results. This course will be offered in distance learning mode only.
This course is offered in conjunction with the Biostatistics Collaboration of Australia (BCA).

Available in 2014

Distance Education - CallaghanSemester 2
Previously offered in 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004
ObjectivesAt 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
ContentBy 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.
Replacing Course(s)N/A
TransitionN/A
Industrial Experience0
Assumed KnowledgeEpidemiology (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.
Modes of DeliveryDistance Learning : Paper Based
Teaching MethodsSelf Directed Learning
Assessment Items
Essays / Written AssignmentsAssignments 60% (Two assignments worth 30% each). Shorter exercises and online quizzes 40%.
Contact HoursSelf Directed Learning: for 6 hour(s) per Week for Full Term
Compulsory Components
Requisite by EnrolmentThis course is only available to students enrolled in the Graduate Diploma in Medical Statistics or Master of Medical Statistics programs.
Timetables2014 Course Timetables for BIOS6070