Units / ETX5500
ETX5500 · High dimensional data analysis
2026 Handbook6 credit pointsLevel 5Department of Econometrics and Business Statistics
Last checked: 23 Aug 2026 UTCOverview
In many fields of business, analysts must deal with data on many variables, for example, surveys with a large number of questions. In such cases, statistical tools known as multivariate methods must be used to analyse the data and drive business decisions. This unit covers such methods in three sections: Cluster Analysis can be used to identify and predict differences between groups such as between distinct classes of customers or products; Principal Components Analysis, Correspondence Analysis and Multidimensional Scaling are dimension reduction methods that help analysts to visualise complicated datasets; and finally, Factor Analysis is used to explain and predict business outcomes.
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Caulfield | Second semester | Teaching activities are on-campus (ON-CAMPUS) |
| Clayton | First semester | Activities scheduled as a mix of on-campus and online activities (BLENDED) |
Assessment
The Handbook lists an examination for this unit.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | 1 - Quiz / Test | Quiz / Test | 15% | — |
| 2 | 2 - Written | Written | 20% | — |
| 3 | 3 - Exercise | Exercise | 15% | — |
| 4 | 4 - Examination | Examination | 50% | — |
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prohibitions
- ETX3500 — High dimensional data analysis
prerequisite
- ETC1010 — Introduction to data analysis
- ETC2420 — Statistical thinking
- ETC3550 — Applied forecasting
- ETC5242 — Statistical thinking
- ETC5510 — Introduction to data analysis
- ETC5550 — Applied forecasting
- ETF3231 — Business forecasting
- ETF5231 — Business forecasting
- ETF5922 — Data visualisation and communication
- ETW2001 — Foundations of data analysis
- ETX2250 — Data visualisation and communication
- FIT5197 — Statistical data modelling
- ETF2020 — Statistical foundations of business analytics
- ETF2100 — Introductory econometrics
- ETF5202 — Statistical foundations of business analytics
- ETF5910 — Introductory applied econometrics
- ETC2410 — Introductory econometrics
- ETF5912 — Data analysis in business
- ETC5241 — Introductory econometrics
Joined by OR.
Learning outcomes
- demonstrate an understanding of the role that multivariate statistical techniques such as factor analysis, structural equation modelling, logistic regression, categorical data analysis, cluster analysis, multidimensional scaling and correspondence analysis play in uncovering relationships and patterns in survey data
- appraise the strengths and limitations of these techniques
- apply tools in R to generate solutions for the appropriate statistical techniques
- demonstrate skills in using the appropriate statistical techniques from a user and provider perspective
- demonstrate skills in communicating the results of the analysis so that decision making can be implemented.
Workload
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.
| Activity | Duration |
|---|---|
| Tutorials | 12 hours |
| Seminars | 24 hours |
| Workshops | 12 hours |
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