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Principles of Statistical Thought - NMSA260

Title:	Principy statistického myšlení
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2023
Semester:	winter
E-Credits:	2
Hours per week, examination:	winter s.:0/2, C [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech, English
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	doc. RNDr. Ivan Mizera, CSc.
Class:	M Bc. FM M Bc. FM > Doporučené volitelné M Bc. FM > 2. ročník M Bc. OM M Bc. OM > Doporučené volitelné M Bc. OM > 2. ročník
Classification:	Mathematics > Probability and Statistics

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Annotation -

Last update: doc. Ing. Marek Omelka, Ph.D. (01.06.2023)

Principles of statistical thought in obtaining conclusions under uncertainty will be exposed on selected real examples of decision, learning, and prediction problems.

Aim of the course -

Last update: doc. RNDr. Ivan Mizera, CSc. (24.05.2023)

The objective of the course is to introduce statistical approaches to the historical and recent problems where uncertainty plays a crucial role, with an emphasis on general principles.

Course completion requirements -

Last update: doc. RNDr. Ivan Mizera, CSc. (15.10.2023)

Active participation in classes (max 3 absences) and submitting the final written report as specified. The nature of these requirements precludes any possibility of additional attempts to obtain the class credit.

Literature - Czech

Last update: doc. RNDr. Ivan Mizera, CSc. (01.06.2023)

Anděl, J.: Statistické úlohy, historky a paradoxy Matfyzpress, Praha 2018.

Teaching methods -

Last update: RNDr. Jitka Zichová, Dr. (09.05.2018)

Seminar.

Syllabus -

Last update: doc. RNDr. Ivan Mizera, CSc. (08.10.2022)

1. Basic concepts of probability and statistics: random variable and its distribution, Bayes theorem, correlation

2. Linear regression, contingency tables

3. Data visualization

4. Paradoxes and classical statistical problems: e.g. Von Neumann’s unfair coin, voting paradoxes, German tank problem

5. Practical examples of application and correct interpretation of statistical models from disciplines including medicine, industrial production, sport, criminology, education, etc.