Statistical Data Analysis 1

Garant předmětu:

Miroslav Myška

Lecturer:

Miroslav Myška

Tutor:

Miroslav Myška

Supervisor:

Department of Physics

Synopsis:

The course is primarily focused on practical application of methods of experimental data analysis. Students obtain knowledge of different statistical methods and their usage, fitting methods, and testing of hypothesis. The course quickly recapitulates basis of mathematical probability theory but it is recommended to attend a full course of the mathematical probability.

Requirements:

Syllabus of lectures:

1. Basic concepts of mathematical and Bayesian statistics.

2. Probability distributions for experimental data.

3. Law of error propagation.

4. Transformation of variables.

5. Estimation of unknown parameters – maximum likelihood, least squares and moment methods.

6. Law of large numbers, central limit theorem, convergences.

7. Basics of data analysis in ROOT program and its packages – RooFit, PyRoot, Minuit. 8. Examples of testing of hypothesis.

Syllabus of tutorials:

Study Objective:

Study materials:

Key references:

[1] L. Lista, Statistical Methods for Data Analysis in Particle Physics, Springer, 2017.

[2] G. Cowan, Statistical Data Analysis, Clarendon Press, Oxford, 1998.

Recommended references:

[3] D. S. Sivia, Data Analysis A Bayesian Tutorial, Claredon Press, Oxford, 1998

[4] C. Maña, Probability and Statistics for Particle Physics, Springer, 2017.

Equipment:

Computer classroom, software ROOT

Note:

Time-table for winter semester 2023/2024:

Time-table is not available yet

Time-table for summer semester 2023/2024:

Time-table is not available yet

The course is a part of the following study plans:

• Jaderná a částicová fyzika (compulsory course in the program)
• Kvantové technologie (elective course)