Statistical analysis of experimental data 1100-4ASWD

1. Probability
2. Basic probability distributions and their properties
3. Measurement uncertainties
4. Monte Carlo Methods
5. Parameter Inference
6. Maximum likelihood
7. Least square method
8. Test of hypothesis
9. Significance of Evidence
10. Confidence interwals and limit extraction
11. Multivariate analysis methods

Main fields of studies for MISMaP

physics

Mode

Classroom

Prerequisites (description)

Lecture addressed to students participating in an academic course on Particle Physics and planning to obtain Master’s degree. It is also open to other interested students - knowledge of particle physics is not required. Courses in an elementary calculus and obligatory laboratory practical on the Bachelor’s level are treated as prerequisite.

Course coordinators

Aleksander Żarnecki

Learning outcomes

Knowledge
Student knows basic methods of statistical analyses of data
Student understands limitations of these methods

Skills
Student identifies problems of data analyses in terms of statistical mathematics
Student is able to implement basic methods of statistical analyses of data in simple cases
Student knows how to interpret results of such analyses

Attitude
Student apreciates importance of deep and thorough analysis problems before drawing conclusions and taking decisions

Assessment criteria

Assessment: based on home exercises performed during the semester and the final written exam - minimum of 50% of points collected from exercises and exam (with same weights) is required to pass. Assessment in September will rest on written examination only and will require a candidate to gain minimum 50% of points.

Practical placement

none

Bibliography

1. G. Bohm i G. Zech, Introduction to Statistics and Data Analysis for Physicsts, Verlag Deutsches Elektronen-Synchrotron, 3rd edition [free access: https://bib-pubdb1.desy.de/record/389738];
2. L. Lista, Statistical Methods for Data Analysis in Particle Physics, Springer, 2017;
3. G. Cowan, Statistical Data Analysis, Oxford University Press, Oxford, 1998;
4. M. Bonamente, Statistics and Analysis of Scientific Data, Springer 2017;
5. S. Brandt, Data Analysis: Statistical and Computational Methods for Scientists and Engineers, Springer 2014;
6. C. W. Fabjan, H. Schopper (eds.), Particle Physics Reference Library, Vol. 2, Chapter 15 [free access: https://link.springer.com/chapter/10.1007/978-3-030-35318-6_15];
7. Particle Data Group: Review of particle physics: reviews, tables, and plots - Mathematical tools [free access: http://pdg.web.cern.ch/pdg/pdg.html]

Additional information

Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:

Physics, second cycle programme

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 1100-4ASWD in USOSweb