Multivariate Statistical Analysis

7.5 credits

Course, Master's level, 2ST125

Expand the information below to show details on how to apply and entry requirements.

Location
Uppsala
Pace of study
50%
Teaching form
On-campus
Instructional time
Daytime
Study period
19 January 2026–23 March 2026
Language of instruction
English
Entry requirements

120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science. 7.5 credits linear algebra.

Selection

Higher education credits (maximum 285 credits)

Fees
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
  • First tuition fee instalment: SEK 15,000
  • Total tuition fee: SEK 15,000

Read more about fees.

Application deadline
15 October 2025
Application code
UU-76616

Admitted or on the waiting list?

Registration period
18 December 2025–18 January 2026
Information on registration from the department

Expand the information below to show details on how to apply and entry requirements.

Location
Uppsala
Pace of study
50%
Teaching form
On-campus
Instructional time
Daytime
Study period
18 January 2027–24 March 2027
Language of instruction
English
Entry requirements

120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science. 7.5 credits linear algebra.

Selection

Higher education credits (maximum 285 credits)

Fees
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
  • First tuition fee instalment: SEK 17,250
  • Total tuition fee: SEK 17,250

Read more about fees.

Application deadline
15 October 2026
Application code
UU-76616

Admitted or on the waiting list?

Registration period
21 December 2026–11 January 2027
Information on registration from the department

About the course

This course provides you with the foundations of basic multivariate statistical procedures, particularly focusing on analysis techniques (using R software) to handle real data. It includes the extension of univariate methods to the multivariate level such as multivariate regression and multivariate analysis of variance, as well, as one- and two-sample inference for mean vectors and covariance matrices. It further extends to essentially multivariate techniques such as principal components analysis, canonical correlation analysis, classification analysis etc.

After taking this course, you are expected to be able to handle, analyse and interpret multivariate data of small to moderate dimensions in real-life problems.

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