Statistical Data Analysis 1
Code  Completion  Credits  Range 

02SZD1  Z,ZK  4  2P+2C 
 Lecturer:
 Miroslav Myška (guarantor)
 Tutor:
 Miroslav Myška (guarantor)
 Supervisor:
 Department of Physics
 Synopsis:

Abstract:
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:

Outline:
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:
 Timetable for winter semester 2020/2021:
 Timetable is not available yet
 Timetable for summer semester 2020/2021:
 Timetable is not available yet
 The course is a part of the following study plans: