Statistical Methods in Physics, 5 credits
Academic year 2022/2023
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Autumn 2022, 33%, Campus
Start date: 31 October 2022
End date: 15 January 2023
Application deadline: 19 April 2022
Application code: UU-13053 Application
Language of instruction: The course will be taught in English, if needed
Location: Uppsala
Selection: Higher education credits in science and engineering (maximum 240 credits)
Registration: 17 October 2022 – 30 October 2022
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Autumn 2022, Mixed, 33%, Distance learning
Start date: 31 October 2022
End date: 15 January 2023
Application deadline: 19 April 2022
Application code: UU-13111 Application
Language of instruction: The course will be taught in English, if needed
Location: Flexible
Selection: Higher education credits in science and engineering (maximum 240 credits)
Outline for distance course: Communication between teachers and students is done using the learning management system and e-meeting tools. A computer with a stable internet connection and webcam is required. It will be possible to attend some of the sessions of campus, for those that want to.
Number of mandatory meetings on campus: 0
Number of voluntary meetings on campus: 0
Registration: 17 October 2022 – 30 October 2022
Entry requirements: 120 credits with basic statistics and 60 credits in physics. Proficiency in English equivalent to the Swedish upper secondary course English 6.
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 or tuition fees. Formal exchange students will be exempted from tuition fees, as well as the application fee. Read more about fees.
Application fee: SEK 900
Tuition fee, first semester: SEK 12,083
Tuition fee, total: SEK 12,083
About the course
The course gives an understanding of and practical skills with statistical methods that are used in physics. The course contains Bayesian vs. frequentistic statistics, uncertainties, probability distributions, expectation value and variance, parameter estimation, hypothesis testing, basic orientation on common software tools, numerical minimising procedures, simple Monte Carlo generators and unfolding of functions from data.