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.