sta3as applied statistics
Credit points: 15
The purpose of STA3AS is to equip graduates with an in depth understanding of modern statistical methods in the following three key topics: 1. Sample surveys with an emphasis on simple random sampling and stratified random sampling. 2. Multivariate analysis with an emphasis on inference for the multivariate mean, checking for underlying multivariate normality, principal component analysis and discriminant analysis.This topic includes an introduction/review of common linear algebra results. 3. Time series analysis with an introduction into the theoretical foundation of Box-Jenkins univariate time series models which form a basis for empirical work with time series data. The software package used in this subject is R.
SchoolSchool Engineering&Mathematical Sciences
Subject Co-ordinatorNatalie Karavarsamis
Available to Study Abroad StudentsYes
Subject year levelYear Level 3 - UG
Prerequisites STA2MD or STM2PM or STA2MDA
Incompatible subjects STA4AS
|Author and Year
|Printed text available from University Bookshop
|Paul Kabaila and Luke Prendergast
|Department of Mathematics and Statistics
|Applied Multivariate Statistical Analysis
|Johnson, R.A. and Wichern, D.W.
|5TH ED, PEARSON, 2002
|Mathematical Statistics and Data Analysis
|3RD EDN., DUXBURY, 2007.
|Time Series Analysis: Forecasting and Control
|Box, G.E.P. and Jenkins, G.M.
|REVISED ED., HOLDEN-DAY, 1976.
Graduate capabilities & intended learning outcomes
01. Present clear, well structured proofs of important fundamental results in sample surveys, multivariate analysis and Box-Jenkins univariate time series analysis. This includes clear and concise use of statistical and mathematical vocabulary and notation.
- Weekly problem classes involve theoretical derivations of results introduced in lectures. 5 assignments consist of at least 50% assessed theoretical derivations.
02. Describe and use key sample survey, multivariate analysis and Box-jenkins univariate time series analysis tools including a justification of appropriate usage based on known model/data conditions
- Appropriate usage of methodologies is discussed and modelled via examples in lectures. Weekly practice classes illustrate this usage.
03. Understand some methods of model checking in the context of multivariate analysis.
- In the lectures and practice classes in the multivariate analysis section of the subject introduce some methods of model checking.
04. Present clear written communications of statistical results in a manner which can be understood by a scientist who fully understands the variables in the associated data set, but who has only a basic understanding of statistics.
- Weekly practice classes in part involve students writing simple evidence based conclusions. Some assignments also partly require students to prepare such simple conclusions.
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Melbourne, 2019, Semester 2, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorNatalie Karavarsamis
PracticalWeek: 31 - 43
One 1.0 hours practical per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
LectureWeek: 31 - 43
Three 1.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
|3-hour short answer Final Examination
|02, 03, 04, 01
|5 Assignments (approx. 240 words each)
|03, 04, 02, 01