Unit (2019)
Information on this page, including unit offerings, is from the 2019 academic year.
Statistical Design and Data Analysis (MAS353)
Organisational Unit  Information Technology, Mathematics and Statistics  
Credit Points  3  
Availability  MURDOCH: S2internal, S2external  
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 ttests 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 


Appears in these Minors  Applied Statistics Fisheries Science 

Internet Access Requirements  Murdoch 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: S2External MURDOCH: S2Internal  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 