# Experimental Design and Data Analysis (MAST10011)

Undergraduate level 1Points: 12.5On Campus (Parkville)

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## Overview

Year of offer 2017 Undergraduate Level 1 MAST10011 Parkville Semester 1Semester 2 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.

## 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.

## Eligibility and requirements

None

None

### Non-allowed subjects

Students may only gain credit for one of:

• Code Name Teaching period Credit Points
MAST10010 Data Analysis 1
Semester 2
12.5
Data Analysis 1
• Code Name Teaching period Credit Points
MAST10011 Experimental Design and Data Analysis
Semester 1
Semester 2
12.5
Experimental Design and Data Analysis
• Code Name Teaching period Credit Points
ECON10005 Quantitative Methods 1
Semester 1
Semester 2
12.5
Quantitative Methods 1

Students who have completed

Code Name Teaching period Credit Points
MAST20005 Statistics
Semester 2
12.5
Statistics may not enrol in this subject for credit.

### 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

## Assessment

### Description

• 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 On Campus — Parkville 3 x one hour lectures per week, 1 x one hour practice class per week, 1 x one hour computer laboratory class per week 170 hours 27 February 2017 to 28 May 2017 10 March 2017 31 March 2017 5 May 2017 23 June 2017

### Semester 1 contact information

• Semester 2
Principal coordinator Yao-ban Chan On Campus — Parkville 3 x one hour lectures per week, 1 x one hour practice class per week, 1 x one hour computer laboratory class per week 170 hours 24 July 2017 to 22 October 2017 4 August 2017 31 August 2017 22 September 2017 17 November 2017

## Time commitment details

Estimated total time commitment of 170 hours

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.

## Further information

• Texts

None.

### Recommended texts and other resources

B. Rosner, Fundamentals of Biostatistics, 8th Edition, Cengage Learning, 2015.

• Subject notes

This subject is only available to students enrolled in the Bachelor of Biomedicine degree or the Bachelor of Biomedical Science (pre-2008 degree)

• Available through the Community Access Program

### About the Community Access Program (CAP)

This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

• Available to Study Abroad and Exchange students

This subject is available to students studying at the University from overseas institutions on exchange and study abroad.

Last updated: 19 October 2018