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Principles of Statistical Reasoning - NMSA260

Title:	Principy statistického myšlení
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2024
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
Teaching methods:	full-time

Guarantor:	doc. RNDr. Jiří Dvořák, Ph.D.
Teacher(s):	doc. RNDr. Jiří Dvořák, Ph.D.
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

Opinion survey results Examination dates WS schedule Noticeboard

Annotation -

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

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

Aim of the course -

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.

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

Course completion requirements -

Active participation in classes (max. 3 absences) and three short homework assignments. The nature of these requirements precludes any possibility of additional attempts to obtain the class credit.

Last update: Dvořák Jiří, doc. RNDr., Ph.D. (16.07.2024)

Literature - Czech

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

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

Teaching methods -

Seminar.

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

Syllabus -

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.

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