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STA3200 Multivariate Statistical Methods

Semester 1, 2016 Online
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Agric, Comp and Environ Sciences
Student contribution band : Band 2
ASCED code : 010103 - Statistics

Contents on this page


Examiner: Rachel King


Pre-requisite: STA2300


Statistics is concerned with the process of making sense out of data. It is the study of uncertainty and is concerned with the process of decision making in the face of uncertainty. As our ability to collect, accumulate and access data increases so does the Volume (amount), Variety (of types, sources and resolutions of data), Velocity (speed of data generation and handling) and Veracity (amount of noise and processing errors) of the data sets we wish to analyse and extract valuable information from. Variety creates wide or high-dimensional data sets that may require specific analytic approaches in order to distinguish useful patterns or develop predictive models for decision making.


This course covers some of the statistical concepts and methodologies appropriate for the analysis of large and/or high dimensional data sets. Students will learn the mathematical foundation of a number of statistical methods, the benefit and limitations of each method, how to correctly apply these methods using statistical software and how to assess the effectiveness of given analyses for given data sets.


On successful completion of this course students should be able to:

  1. Demonstrate advanced and integrated understanding of high-dimensional data sets.
  2. Apply the knowledge of high-dimensional data sets in the evaluation and choice of appropriate statistical methods.
  3. Apply the knowledge of a range of computational methods and diagnostic techniques to test hypotheses and evaluate and interpret the output correctly and in context.
  4. Analyse critically the capabilities of and implement R software as a statistical package in creating and analysing different statistical methods.
  5. Synthesise and interpret analyses and communicate the results of analyses to a diverse audience to aid decision making.


Description Weighting(%)
1. Review matrix algebra, linear regression and confidence intervals. Introduction to the features of high-dimension data, graphical summaries and R programming. 10.00
2. Multidimensional Scaling and Cluster Analysis (k-means and hierarchical). 20.00
3. Discriminant Function Analysis and Canonical Correlation Analysis. 20.00
4. Principle Components Analysis and Factor Analysis. 20.00
5. Principle Components Regression 10.00
6. Partial Least Squares Regression and Partial Least Squares Discriminant Analysis. 20.00

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. (

  • Manly, BFJ 2005, Multivariate Statistical Methods: A Primer, 3rd edn, Chapman & Hall/CRC, London.

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 expectations

Activity Hours
Assessments 58.00
Directed Study 52.00
Private Study 60.00

Assessment details

Description Marks out of Wtg (%) Due Date Notes
Assignment 1 100 25 18 Apr 2016
Assignment 2 100 25 23 May 2016
Examination 100 50 End S1

Important assessment information

  1. Attendance requirements:
    There are no attendance requirements for this course. However, 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:
    To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item.

  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:
    To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course.

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

  6. Examination information:
    In a Restricted Examination, candidates are allowed access to specific materials during the examination. The only materials that candidates may use in the restricted examination for this course are:
    ? writing materials (non-electronic and free from material which could give the student an unfair advantage in the examination);
    ? calculators which cannot hold textual information (students must indicate on their examination paper the make and model of any calculator(s) they use during the examination)
    ? Students whose first language is not English, may, take an appropriate unmarked non-electronic translation dictionary (but not technical dictionary) into the examination. Dictionaries with any handwritten notes will not be permitted. Translation dictionaries will be subject to perusal and may be removed from the candidate's possession until appropriate disciplinary action is completed if found to contain material that could give the candidate an unfair advantage.
    ? A Formula sheet will be provided by the Examiner as part of the examination paper.

  7. Examination period when Deferred/Supplementary examinations will be held:
    Any Deferred or Supplementary examinations for this course will be held during the next examination period.

  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. Referencing in Assignments must comply with the Harvard (AGPS) referencing system. This system should be used by students to format details of the information sources they have cited in their work. The Harvard (APGS) style to be used is defined by the USQ library?s referencing guide. These policies can be found at //

Other requirements

  1. Computer, e-mail and Internet access:
    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 //

  2. Students can expect that questions in assessment items in this course may draw upon knowledge and skills that they can reasonably be expected to have acquired before enrolling in this course. This includes knowledge contained in pre-requisite courses and appropriate communication, information literacy, analytical, critical thinking, problem solving or numeracy skills. Students who do not possess such knowledge and skills should not expect the same grades as those students who do possess them.