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Multivariate Statistics (6544.4)

Level: Undergraduate Third Year Level
Credit Points: 3
HECS Bands: 2, 4
Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology

Availability

Unit Outlines

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  • Semester 1, 2019, ON-CAMPUS, BRUCE (185230) - View
  • Semester 1, 2018, ON-CAMPUS, BRUCE (181702) - View
  • Semester 2, 2016, ON-CAMPUS, BRUCE (150623) - View

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Syllabus

This unit deals with multivariate statistical and data analytical methods for analysing multidimensional and big data. The practical background of analyses will be presented and computer packages will be used to carry out the analyses. Topics will be selected from descriptive statistics, data visualisation, descriptive data mining, regression and classification, predictive data mining, principal components analysis and other dimension reduction techniques. Applications to data and problems in business, natural and social sciences will be illustrated.

Learning Outcomes

On successful completion of this unit, students will be able to:

1. Describe the nature and properties of multivariate and big data;

2. Select and explain the appropriate statistical analysis for a given purpose and data set;

3. Evaluate the appropriateness and validity of a multivariate and analytic method; and

4. Produce and interpret the results by using a computer package.

Assessment Items

Contact Hours

A 2-hour lecture and a 2-hour lab per week.

Prerequisites

6540 Introduction to Statistics OR 5123 Business Statistics OR 1809 Data Analysis in Science

Corequisites

None.

Assumed Knowledge

None.

Incompatible Units

None.

Equivalent Units

None.



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