Multivariate Statistical Methods, 6 credits

Multivariata statistiska metoder, 6 hp


Main field of study


Course level

Second cycle

Course type

Single subject and programme course


Krzysztof Bartoszek

Course coordinator

Krzysztof Bartoszek

Director of studies or equivalent

Ann-Charlotte Hallberg

Available for exchange students



ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Timetable module Language Campus ECV
Single subject course (Half-time, Day-time) Autumn 2019 201945-202003 3 English Linköping, Valla
Single subject course (Half-time, Day-time) Autumn 2019 201945-202003 3 English Linköping, Valla
F7MSL Statistics and Machine Learning, Master´s Programme 1 (Autumn 2019) 201945-202003 3 English Linköping, Valla E
F7MSL Statistics and Machine Learning, Master´s Programme 3 (Autumn 2019) 201945-202003 3 English Linköping, Valla E

Main field of study


Course level

Second cycle

Advancement level


Course offered for

  • Masters Programme in Statistics and Machine Learning

Entry requirements

A bachelor’s degree in one of the following subjects: statistics, mathematics, applied mathematics, computer science, engineering, or equivalent. Completed courses in calculus, linear algebra, statistics and programming are required.
The student should also have passed at least one intermediate course in each of the following areas: 
-statistical inference;
-linear statistical models.

English corresponding to the level of English in Swedish upper secondary education (English 6/B).
Exemption from Swedish 3/B.

Intended learning outcomes

After completion of the course, the student should 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.

Course 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 model 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 regions

- principal component analysis and large sample inference

- factor analysis,

- canonical correlation analysis and large sample inference,

- MANOVA models. 

Teaching and working methods

The teaching comprises lectures, seminars, and computer exercises complemented by self-studies. Lectures are devoted to presentations of theories, concepts and methods. Computer exercises provide practical experience of analyzing multivariate data. The seminars comprise student presentations and discussions of computer assignments.
Language of instruction: English. 


Written reports on computer assignments. Active participation at the seminars. A final oral or written examination. Detailed information about the examination can be found in the course’s study guide.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.



Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.


Institutionen för datavetenskap
Code Name Scope Grading scale
TENT Examination 3 credits EC
LAB1 Laboratory work 3 credits EC
FRIV Voluntary examination 0 credits EC
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