Master’s studies

Syllabus for Statistical Methods in Physics

Statistiska metoder i fysiken

Syllabus

  • 5 credits
  • Course code: 1FA357
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Physics A1N
  • Grading system: Fail (U), 3, 4, 5.
  • Established: 2010-03-18
  • Established by: The Faculty Board of Science and Technology
  • Revised: 2017-05-04
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 30, 2017
  • Entry requirements: 120 credits with basic statistics and 60 credits in physics.
  • Responsible department: Department of Physics and Astronomy

Learning outcomes

When the course is completed the student should be able to

  • account for the difference between Bayesian and frequentistic statistics
  • compare different data,judge the degree of compatibility and correctly treat uncertainties
  • carry out function minimisation, both analytically and numerically
  • establish confidence intervals
  • estimate parameters using established methods
  • perform hypothesis testing and relate the result to probability
  • utilize common software tools, including Monte Carlo generators, for statistical analysis
  • perform an unfolding of a function from data

Content

Practical skills with statistical methods that are used in physics. Bayesian vs. frequentistic statistics, Uncertainties, Probability distributions, Expectation value and variance. Parameter estimation: Method of Moments, Maximum-Likelihood, Least Squares. Hypothesis testing: Chi Square, Signal vs. Background, Kolmogorov-Smirnov test. Function minimisation with constraints. Basic orientation on common software tools, numerical minimizing procedures, simple Monte Carlo generators and unfolding of functions from data. 

Instruction

 Online lectures, IRL lectures with focus on problem solving. Workshops. 

Assessment

Hand-in exercises with oral presentations at the workshop. Active participation at lectures and workshops.

Reading list

Applies from: week 20, 2017

  • Bevington, Philip R.; Robinson, D. Keith Data reduction and error analysis for the physical sciences

    3. ed.: Boston: McGraw-Hill, cop. 2003

    Find in the library

    Mandatory