Applied Statistical Methods

7.5 credits

Course, Bachelor's level, 2ST094

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

Location
Uppsala
Pace of study
100%
Teaching form
On-campus
Instructional time
Daytime
Study period
7 December 2026–17 January 2027
Language of instruction
English
Entry requirements

At least 15 credits from Statistics A, 30 credits

Selection

Final school grades (60%) - Swedish Scholastic Aptitude Test (40%)

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 12,375
  • Total tuition fee: SEK 12,375

Read more about fees.

Application deadline
15 April 2026
Application code
UU-26628

Admitted or on the waiting list?

Registration period
23 July 2026–23 August 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
100%
Teaching form
On-campus
Instructional time
Daytime
Study period
3 May 2027–6 June 2027
Language of instruction
English
Entry requirements

At least 15 credits from Statistics A, 30 credits

Selection

Final school grades (60%) - Swedish Scholastic Aptitude Test (40%)

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 12,375
  • Total tuition fee: SEK 12,375

Read more about fees.

Application deadline
15 October 2026
Application code
UU-76628

Admitted or on the waiting list?

Registration period
19 April 2027–26 April 2027
Information on registration from the department

About the course

This course in applied statistics focuses on data analysis using statistical methods. The course consists of a number of cases (homework assignments) and for each of these, you hand in a written report and do an oral presentation of the report. Examination of the course is based on the homework assignments; no traditional exam is given.

You will learn to apply appropriate statistical tools to various data sets and research questions. The course requires a solid understanding of basic probability theory and statistical inference, basic econometrics and time series analysis. Examples of methods used are multiple regression, logistic regression and ARIMA models.

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