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STA8180 Advanced Statistics A

Semester 1, 2015 External Toowoomba
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Agric, Comp and Environ Sciences

Contents on this page


Examiner: Rachel King
Moderator: Shahjahan Khan

Other requisites

These topics are offered in both STA8180 and STA8190. Within this unit students can choose one of a number of topics each of which has different prerequisites (or equivalent) as outlined below, or on approval of the examiner. Students must choose a different topic in each course (STA8180 and STA8190)

Time Series Analysis: STA3300
Data Mining: CSC1401 and MAT1101
Multivariate Statistical Methods: STA3300
Applied Experimental Design STA2300 Applied Statistical Models STA3300
Bayesian Statistics: STA2301 and STA2302

Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at //


This course provides flexibility in postgraduate programs to cater for the widely varying interests and chosen specialisations of students. Statisticians need to be proficient in a wide range of statistical techniques. Many of these are either only touched on or omitted from undergraduate programs. An opportunity to broaden the students' knowledge-base with more advanced statistical techniques is provided in this course. For example, Time Series Analysis has application to a wide variety of processes, including econometric, actuarial, commercial, industrial, agricultural, environmental, meteorological and medical processes. Data mining is an interdisciplinary field which brings together techniques of machine learning, database information retrieval, mathematics and statistics. In Experimental Design, the proper design, implementation and analysis of results of experiments are of vital importance in many disciplines. The validity and reliability of research findings can be severely compromised if a poor design or experimental procedure is followed. In Applied Statistical Models, students seeking to specialise in statistics will need to understand and be competent in Linear Models and generalised linear model techniques.


This course provides the opportunity for a student to pursue an area of study that will complement the other studies in the student's program. Typically the course will consist of specialised investigations extending knowledge and skills in a certain area. The studies could involve, for example, directed readings, a project (where appropriate), or some other approved activity which would complement the student's studies in the program.


On completion of this course students will be able to

  1. demonstrate advanced knowledge and skills in the study area chosen.


Description Weighting(%)
1. The content of the course may be chosen to be one of the following areas; other choices may be available. The content of the course may vary from student to student. The weighting of the sub-topics within this unit depends on the topic chosen and will be discussed with the examiner

Time Series Analysis: This course consists of advanced studies in time series analysis. Topics will include: identification, estimation, testing and forecasting for univariate and multivariate models of time series; the spectral representation of a time series; non-linear models, including identification, estimation, testing and forecasting; cointegrated models.

Data Mining: Data mining aims at finding useful regularities or patterns in large data sets generated in modern management and science. This course covers the main data mining methods, including clustering, classification, association rules mining, text indexing algorithms, and recent techniques for mining. The methods are developed and applied to databases representative of applications in genetics and marketing.

Multivariate Statistical Methods: Multivariate statistics techniques are introduced with an emphasis on the correct application and interpretation of analyses. Content is based on the textbook Multivariate Statistical Methods: A primer by Bryan F. J. Manly and all analysis is performed using R software. Topics include: displaying multivariate data, MANOVA, Principle Components Analysis (PCA), Factor Analysis (FA), Discriminant Function Analysis (DFA), Canonical Correlation Analysis (CCA), multivariate distance measures, Cluster Analysis and Multi-dimensional Scaling (MDS). Similarities and differences between these methods, their strengths and limitations and the assumptions underlying their use are explored.

Bayesian Statistics: The fundamental idea of Bayesian Statistics comes from reverend Thomas Bayes the eighteenth century. This has been extended to create the quickly expanding field of Bayesian estimation and inferential methods, quickly extending into every quantitative area of research. Bayesian analyses mix the observed data with the prior distribution of the relevant parameters of the underlying model. It allows inference about population parameters in any statistical model in a slightly different way than the classical statistics because of incorporating prior or expert information. The course covers various estimation and test of hypothesis methods for different models using Bayesian approach. It also considers applications of Bayesian methods in diverse areas and uses R package for computational purposes.

Applied Experimental Design (see STA 3300 for topics)

Applied Statistical Models (see STA3301 for topics)

Text and materials required to be purchased or accessed

ALL textbooks and materials available to be purchased can be sourced from USQ's Online Bookshop (unless otherwise stated). (

Please contact us for alternative purchase options from USQ Bookshop. (

  • Depending on the topic chose one of the following: Time Series Analysis: no text Data Mining: no text Multivariate Statistical Methods: Multivariate Statistical Methods: A primer by Bryan F.J Manly (Chapman & Hall) Applied Experimental Design (see STA 3300 for text) Applied Statistical Models (see STA3301 for text) Bayesian Statistics: Introduction to Bayesian Statistics, 2nd Ed, by William M. Bolstad.

Reference materials

Reference materials are materials that, if accessed by students, may improve their knowledge and understanding of the material in the course and enrich their learning experience.

Student workload requirements

Activity Hours
Assessments 20.00
Private Study 90.00
Project Work 40.00
Supervisor Consultation 15.00

Assessment details

Description Marks out of Wtg (%) Due Date Notes
Assignment 1 40 40 02 Mar 2015
Assignment 2 (Project) 60 60 02 Mar 2015

Important assessment information

  1. Attendance requirements:
    Attendance requirements: There are no attendance requirements for this course. It is the students' responsibility to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

  2. Requirements for students to complete each assessment item satisfactorily:
    Requirements for students to complete each assessment item satisfactorily: To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks or a grade of at least C.

  3. Penalties for late submission of required work:
    Students should refer to the Assessment Procedure (point 4.2.4)

  4. Requirements for student to be awarded a passing grade in the course:
    Requirements for student to be awarded a passing grade in the course: To be assured of receiving a passing grade a student achieve at least 50% of the total weighted marks available for the course.

  5. Method used to combine assessment results to attain final grade:
    Method used to combine assessment results to attain final grade: The final grades for students will be assigned on the basis of the weighted aggregate of the marks obtained for each of the summative assessment items in the course.

  6. Examination information:
    There is no examination in this course.

  7. Examination period when Deferred/Supplementary examinations will be held:
    There will be no Deferred or Supplementary examinations in this course.

  8. University Student Policies:
    Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at

Assessment notes

  1. The due date for an assignment is the date by which a student must despatch the assignment to USQ via electronic submission on the course website. The onus is on the student to provide proof of the despatch date, if requested by the Examiner. Students must retain a copy of each item submitted for assessment. This must be produced within five days if required by the Examiner.

  2. Students who, for medical, family/personal, or employment-related reasons, are unable to complete an assignment or to sit for an examination at the scheduled time may apply to defer an assessment in a course. Such a request must be accompanied by appropriate supporting documentation. A temporary grade IDM (Incomplete Deferred Make-up) may be awarded.

  3. Harvard (AGPS) is the referencing system required in this course. Students should use Harvard (AGPS) style in their assignments to format details of the information sources they have cited in their work. The Harvard (AGPS) style to be used is defined by the USQ Library's referencing guide. //