Experimental Design and Data Analysis

Subject MAST10011 (2010)

Note: This is an archived Handbook entry from 2010.

Credit Points: 12.50
Level: 1 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2010:

Semester 1, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Semester 2, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Lectures, practice classes and computer laboratory classes.

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: 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: Estimated total time commitment of 120 hours
Prerequisites: None
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: Students may only gain credit for one of

Students who have completed any of the following may not enrol in this subject for credit

Core Participation Requirements: It is University policy to take all reasonable steps to minimise the impact of disability upon academic study and reasonable steps will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact upon their active and safe participation in a subject are encouraged to discuss this with the relevant subject coordinator and the Disability Liaison Unit.

Coordinator

Dr Owen Jones

Contact

First Year Coordinator

Email: fycoord@ms.unimelb.edu.au

Subject Overview:

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.

Objectives:

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.
Assessment:

One written assignment of up to 20 pages due during semester (9%), three online tests at regular intervals during semester (6%), one 45-minute written computer laboratory test held mid-semester (5%), and a 3-hour written examination in the examination period (80%).
Prescribed Texts: M. M. Triola and M. F. Triola, Biostatistics for the Biological and Health Sciences, Boston, Pearson, 2006.
Breadth Options:

This subject is not available as a breadth subject.
Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

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.
Notes:

This subject is only available to students enrolled in the Bachelor of Biomedicine degree or the Bachelor of Biomedical Science (pre-2008 degree)
Related Course(s): Bachelor of Biomedicine
Related Majors/Minors/Specialisations: Master of Engineering (Geomatics)

Download PDF version.