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Unit (2020)

Information on this page, including unit offerings, is from the 2020 academic year.

Statistical Design and Data Analysis (MAS353)

Organisational Unit Information Technology, Mathematics and Statistics
Credit Points 3
Availability MURDOCH: S2-internal, S2-external
Teaching Timetables Murdoch S2
Description This unit is designed for applied statisticians and scientists who need to apply the principles of statistical science to collect, analyse, interpret and present data of high quality. The rich class of statistical techniques known as regression models, which includes multiple regression, analysis of variance and covariance, and logistic regression are covered. Case studies and student projects explore the use of these techniques on real data from a variety of disciplines using statistical computing packages.
Unit Learning Outcomes Upon successful completion of the unit, students should be able to:
1. Carry out a variety of statistical analyses using statistical software. In particular, students should be able to:
a. Analyse data using the general linear model, including the special cases of t-tests for one and two samples, ANOVA, and multiple regression.
b. Recognise factors, covariates, quadratics and interactions in statistical models and interpret these.
c. Apply regression diagnostics to data to determine whether the assumptions of a general linear model are violated, and determine appropriate courses of action when they are.
d. Analyse data concerning the comparison of two population proportions, odds ratios and contingency tables, and use the techniques of logistic regression for binary and binomial data.
e. Apply statistical design principles to the method of collection of data to answer applied research questions in a variety of disciplines. This includes the use of factorial experiments, blocking, controls and placebos, randomisation, measurement of extra covariate information, and simple, approximate determinations of the sample size required.
f. Learn the basic techniques of linear mixed effects modelling.
g. Use appropriate statistical computer packages such as R to manipulate and analyse data.
2. Present results of statistical analyses of data in technical reports.
3. Explain conceptually the various statistical methods covered in the unit, the correct application of these methods, and interpret statistical software output.
Timetabled Learning Activities Lectures: 3 x 1 hour per week; tutorials: 1 x 1 hour per week.
Unit Learning Experiences This unit uses a mixture of structured activities and assessments to assist students in learning the material covered in the unit. Structured activities include lectures and tutorials, and assessments include assignments, a project, and a final exam. It is essential that internal students should attend lectures and tutorials. External and internal students should review the recorded lectures on Echo360 and study the unit notes and textbook in the progression of the lectures.
Assessment All students' abilities to correctly apply statistical methods will be assessed at regular intervals during the semester via assignments and a project. These assessments are designed to allow students to demonstrate their ability in each of the content areas of the unit and to give them regular feedback on their progress, helping them to identify their areas of strength or weakness during the semester. Assignment solutions and results will be posted progressively on the Learning Management System.
The weightings for assessment items are as follows:
Assignments (4) - 30%
Project - 15%
Final Examination - 55%
Prerequisites MAS222/MAS278 Probability and Statistical Inference OR MAS223 Applied Statistics OR MAS224/MAS230 Biostatistical Methods OR MAS284 Applied Statistics and Process Management.
Exclusions Students may not enrol in this unit and either of MAS180 Introduction to Statistics or MAS183 Statistical Data Analysis concurrently. Students who previously successfully completed MAS374 Statistical Design and Data Analysis cannot enrol in this unit for credit.
Appears in these Courses/Majors:
see individual structures for context
Advanced Mathematics Major Teaching Area (BEd(Sec)) [New in 2019]
Animal Health (BSc) [New in 2015]
Animal Science (BSc) [New in 2014]
Biological Sciences (BSc) [New in 2014]
Biomedical Science (BSc) [New in 2014]
Chemistry (BSc) [New in 2014]
Clinical Laboratory Science (BSc) [New in 2015]
Conservation and Wildlife Biology (BSc) [New in 2014]
Crop and Pasture Science (BSc) [New in 2016]
Engineering Technology (BSc) [New in 2014]
Environmental Management and Sustainability (BSc) [New in 2014]
Environmental Science (BSc) [New in 2014]
Forensic Biology and Toxicology (BSc) [New in 2014]
Genetics and Molecular Biology (BSc) [New in 2014]
Marine Biology (BSc) [New in 2017]
Marine Science (BSc) [New in 2014]
Mathematics and Statistics (BSc) [New in 2014]
Mathematics Major Teaching Area (BEd(Sec)) [New in 2019]
Mineral Science (BSc) [New in 2014]
Physics and Nanotechnology (BSc) [New in 2014]
Sport and Health Science (BSc) [New in 2014]
Appears in these Minors Applied Statistics
Fisheries Science
Internet Access RequirementsMurdoch units normally include an online component comprising materials, discussions, lecture recordings and assessment activities. All students, regardless of their location or mode of study, need to have access to and be able to use computing devices with browsing capability and a connection to the Internet via Broadband (Cable, ADSL or Mobile) or Wireless. The Internet connection should be readily available and allow large amounts of data to be streamed or downloaded (approximately 100MB per lecture recording). Students also need to be able to enter into online discussions and submit assignments online.

Contacts

Unit Coordinator
MAS353
Dr Brenton Clarke
Senior Lecturer

Murdoch Campus
t: 9360 2578
e: B.Clarke@murdoch.edu.au
o: 245.3.029 - Science and Computing, Murdoch Campus
Unit Contacts
MAS353

MURDOCH: S2-External
MURDOCH: S2-Internal
Dr Brenton Clarke
Senior Lecturer

Murdoch Campus
t: 9360 2578
e: B.Clarke@murdoch.edu.au
o: 245.3.029 - Science and Computing, Murdoch Campus
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