Multivariate Statistics
| Program start date | Application deadline |
| 2026-07-06 | - |
Program Overview
Introduction to STAT 394: Multivariate Statistics
Multivariate Statistics is an essential component of Statistics and Data Analysis. This course delves into the fundamental concepts and techniques to tackle complex multivariate data.
Course Details
- Course Code: STAT 394
- Duration: 18 weeks
- Estimated Workload: Approximately 150 hours or 8.3 hours per week
- Points: 15
Course Description
This course covers Principal Component Analysis, Cluster Analysis, Factor Analysis, Discriminant Analysis, Canonical Correlations, the Multivariate General Linear Model, and Multidimensional Scaling. Students will have hands-on experience using statistical software to apply these techniques to real-world datasets.
Learning Objectives
Students who pass this course will be able to:
- Understand the characteristic features of multivariate data.
- Confidently use statistical software to perform multivariate analyses.
- Use graphical tools to find patterns in multivariate data.
- Report results in a comprehensive manner.
How the Course is Taught
- Two lectures and one tutorial per week.
- Designed for in-person study, with a strong recommendation to attend lectures and tutorials on campus.
Assessment
- 5 Assignments: 35%
- 2 Tests: 50%
- Group project: 15%
- Report
- Presentation
Mandatory Requirements
There are no mandatory requirements for this course.
Group Work
The group project will be assessed based on:
- Report quality and content
- Presentation quality and distribution among team members
- Ability to answer questions
Lecture Times and Rooms
- 6 July 2026 to 16 August 2026
- 31 August 2026 to 11 October 2026
What You'll Need to Get
- A computer with LaTeX, BibTeX, RStudio, and R.
- Installation of LaTeX and R libraries.
- Mathematica may be useful but is not mandatory.
Fees
- Domestic students: NZ$953.25
- International students: NZ$5,058.00
Entry Restrictions
- Prerequisites: MATH 277 or (STAT 292, STAT 391)
- Corequisites: None
- Restrictions: None
Taught By
School of Mathematics and Statistics — Faculty of Science and Engineering
Disclaimer
This course outline may be subject to change.
