732A88 · 6 credits · Advanced level (A1F) · Statistik
Multivariate Statistical Methods
Active · next start HT 2026
86%
passed the latest ordinary exam
TEN1 · 14 Jan 2026 · 7 students
86%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 7 students
Part of 1 programme
Exam
TEN1 Examination
- A
- B
- E
- F
Ordinary exams
86% passed · 7 students
- 86%7
Re-exams
No sittings with at least 5 students.
- Get B or A: 71% (7 students)
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB2 Laboratory work
- P
Main rounds
100% passed · 8 students
- 100%8
Later rounds
No sittings with at least 5 students.
Other module
FRI1 Bonus points
- P
Main rounds
100% passed · 8 students
- 100%8
Later rounds
No sittings with at least 5 students.
About the course
Aim
After completing the course, the student shall be able to:
- analyze multivariate data by using appropriate multivariate models,
- account for mathematical models related to various multivariate methods and derive theoretical results from these models,
- apply hypothesis testing and large sample theory to assess the credibility of the results obtained by multivariate models,
- use computer simulations to solve multivariate statistical problems,
- account for different types of covariance structures and their impact on interpretation,
- apply multivariate methods for dimension reduction.
Content
The course comprises the mathematical theory for the multivariate normal distribution and its related distributions, as well as the practical application of this theory to a range of multivariate statistical models and inference problems in statistics, machine learning and engineering.
The course includes:
- matrix algebra, random vectors and matrices
- multivariate normal distribution, mathematical properties of sampling distributions and large sample theory.
- inference of mean vectors, related hypothesis testing models and confidence regionsprincipal component analysis and large sample inference
- factor analysiscanonical correlation analysis and large sample inferenceMANOVA models.
- Examiner
- Krzysztof Bartoszek
- Main field of study
- Statistik