sta4as applied statistics
APPLIED STATISTICS
STA4AS
2017
Credit points: 15
Subject outline
The purpose of STA4AS 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. This subject is co-taught with STA3AS. However, independent research regarding some advanced proofs is required and assessed in STA4AS.
SchoolSchool Engineering&Mathematical Sciences
Credit points15
Subject Co-ordinatorPaul Kabaila
Available to Study Abroad StudentsYes
Subject year levelYear Level 4 - UG/Hons/1st Yr PG
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites STA2MD or STM2PM or STA2MDA
Co-requisitesN/A
Incompatible subjects STA3AS
Equivalent subjectsN/A
Special conditionsN/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Applied Multivariate Analysis | Recommended | Johnson, RA & Wichern, DW 2002 | 5TH ED. PRENTICE-HALL. |
| Readings | Applied Statistics Unit Text | Recommended | Paul Kabaila and Luke Prendergast | AVAILABLE FROM THE BOOKSHOP |
| Readings | Mathematical Statistics and Data Analysis | Recommended | Rice, JA 2007 | 3RD ED. DUXBURY. |
| Readings | Time Series Analysis: Forecasting and Control | Recommended | Box, GEP & Jenkins, GM 1976 | HOLDEN-DAY |
| Readings | Applied Statistics STA3AS/STA4AS | Prescribed | Paul Kabaila and Luke Prendergast | La Trobe University |
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 appropriate use of statistical and mathematical vocabulary and notation.
- Activities:
- Weekly problem classes involve theoretical derivations of results introduced in lectures. 5 assignments consist of at least 50% assessed theoretical derivations.
- Related graduate capabilities and elements:
- Creative Problem-solving(Creative Problem-solving)
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
- Writing(Writing)
- Discipline-specific GCs(Discipline-specific GCs)
02. Understand 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
- Activities:
- Appropriate usage of methodologies is discussed and modelled via examples in lectures. Weekly practice classes illustrate this usage.
- Related graduate capabilities and elements:
- Discipline-specific GCs(Discipline-specific GCs)
- Writing(Writing)
- Critical Thinking(Critical Thinking)
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
- Inquiry/ Research(Inquiry/ Research)
03. Understand some methods of model checking in the context of multivariate analysis.
- Activities:
- In the lectures and practice classes of the multivariate analysis section of the subject introduce some methods of model checking.
- Related graduate capabilities and elements:
- Critical Thinking(Critical Thinking)
- Discipline-specific GCs(Discipline-specific GCs)
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
- Creative Problem-solving(Creative Problem-solving)
04. Present clear written commuincations 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.
- Activities:
- Weekly practice classes in part involve students writing simple evidence based conclusions. Some assignments also partly require students to prepare such simple conclusions.
- Related graduate capabilities and elements:
- Discipline-specific GCs(Discipline-specific GCs)
- Inquiry/ Research(Inquiry/ Research)
- Writing(Writing)
- Critical Thinking(Critical Thinking)
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
05. Independently formulate proofs for key theoretical results presented in the lectures.
- Activities:
- Key theoretical results are presented in the lectures. Some of these results are only accompanied with, at most, a brief description as to how they may be proven. Within each assignment and problem class, STA4AS students will be required to formulate their own proofs of these results. This will involve referencing suitable sources either via the internet or through the library. Students can model their proofs on those provided for other key results that are presented in the lectures. This is a key point of difference with STA3AS.
- Related graduate capabilities and elements:
- Writing(Writing)
- Discipline-specific GCs(Discipline-specific GCs)
- Creative Problem-solving(Creative Problem-solving)
- Inquiry/ Research(Inquiry/ Research)
- Critical Thinking(Critical Thinking)
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
Subject options
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Melbourne, 2017, Semester 2, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorPaul Kabaila
Class requirements
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.
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.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| 3-hour Final Examination | 70 | 01, 02, 04, 03 | |
| 5 Assignments (approx. 240 words each) | 30 | 03, 04, 02, 05, 01 |