|Year of offer||2017|
|Subject level||Undergraduate Level 1|
|Fees||Subject EFTSL, Level, Discipline & Census Date|
This subject provides an understanding of the fundamental concepts of probability and statistics required for experimental design and data analysis in the health sciences. Initially the subject introduces common study designs, random sampling and randomised trials as well as numerical and visual methods of summarising data. It then focuses on understanding population characteristics such as means, variances, proportions, risk ratios, odds ratios, rates, prevalence, and measures used to assess the diagnostic value of a clinical test. Finally, after determining the sampling distributions of some common statistics, confidence intervals will be used to estimate these population characteristics and statistical tests of hypotheses will be developed. The presentation and interpretation of the results from statistical analyses of typical health research studies will be emphasised.
The statistical methods will be implemented using a standard statistical computing package and illustrated on applications from the health sciences.
Intended learning outcomes
On completion of the subject, students should be able to:
- analyse standard data sets, interpreting the results of such analysis and presenting the conclusions in a clear and comprehensible manner;
- understand a range of standard statistical methods which can be applied to biomedical sciences.
- use a statistical computing package to analyse biomedical data;
- choose a form of epidemiological experimental design suitable for a range of standard biomedical experiments.
In addition to learning specific skills that will assist students in their future careers in the health sciences, they will have the opportunity to develop, generic skills that will assist them in any future career path. These include:
- problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- collaborative skills: the ability to work in a team;
- time management skills: the ability to meet regular deadlines while balancing competing commitments;
- computer skills: the ability to use statistical computing packages.
Eligibility and requirements
Students may only gain credit for one of:
Code Name Teaching period Credit Points MAST10010 Data Analysis 1Semester 2 12.5
Code Name Teaching period Credit Points MAST10011 Experimental Design and Data AnalysisSemester 1Semester 2 12.5
Code Name Teaching period Credit Points ECON10005 Quantitative Methods 1Semester 1Semester 2 12.5
Students who have completed
|Code||Name||Teaching period||Credit Points|
Core participation requirements
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
- 1 Hand-written assignments (part 1) with use of software due weeks 3 and 4 (5%)
- 1 Hand-written assignments (part 2) with use of software due weeks 7 and 8 (5%)
- One 40 minute computer-based in-class test due week 11 (10%)
- Best 10 (of 11) online quizzes held weekly from week 2 (10%)
- 3-hour written examination in the examination period (70%)
Quotas apply to this subject
Dates & times
- Semester 1
Principal coordinator Yao-ban Chan Mode of delivery On Campus — Parkville Contact hours 3 x one hour lectures per week, 1 x one hour practice class per week, 1 x one hour computer laboratory class per week Total time commitment 170 hours Teaching period 27 February 2017 to 28 May 2017 Last self-enrol date 10 March 2017 Census date 31 March 2017 Last date to withdraw without fail 5 May 2017 Assessment period ends 23 June 2017
Semester 1 contact information
- Semester 2
Principal coordinator Yao-ban Chan Mode of delivery On Campus — Parkville Contact hours 3 x one hour lectures per week, 1 x one hour practice class per week, 1 x one hour computer laboratory class per week Total time commitment 170 hours Teaching period 24 July 2017 to 22 October 2017 Last self-enrol date 4 August 2017 Census date 31 August 2017 Last date to withdraw without fail 22 September 2017 Assessment period ends 17 November 2017
Semester 2 contact information
Time commitment details
Estimated total time commitment of 170 hours
Additional delivery details
An enrolment quota of 270 students applies per semester. Students will be enrolled in Experimental Design and Data Analysis in the opposite semester to which they are enrolled in Mathematics for Biomedicine. Please refer to the Handbook entry for MAST10016 Mathematics for Biomedicine for further information.
Recommended texts and other resources
B. Rosner, Fundamentals of Biostatistics, 8th Edition, Cengage Learning, 2015.
This subject is only available to students enrolled in the Bachelor of Biomedicine degree or the Bachelor of Biomedical Science (pre-2008 degree)