Methods of Mathematical Statistics - NMAI061
Title: Metody matematické statistiky
Guaranteed by: Department of Software Engineering (32-KSI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2023
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/1, C+Ex [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
Teaching methods: full-time
Guarantor: doc. RNDr. Zdeněk Hlávka, Ph.D.
Class: Informatika Mgr. - Teoretická informatika
Informatika Mgr. - Softwarové systémy
Informatika Mgr. - Matematická lingvistika
Classification: Mathematics > Probability and Statistics
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Annotation -
The main aim is to enhance the knowledge from the course Probability and statistics. Attention will be paid especially to the principles of estimation and hypothesis testing, to the theory and applications of the linear model, and to an overview of other useful statistical methods.
Last update: T_KPMS (20.05.2009)
Aim of the course -

Basic statistical methods, estimation and testing and their use in practice will be presented.

Last update: Antoch Jaromír, prof. RNDr., CSc. (06.02.2024)
Course completion requirements -

Conditions for obtaining the credit (zápočet): Active participation in the exercises and successful solving of a written assignment.

Last update: Antoch Jaromír, prof. RNDr., CSc. (06.02.2024)
Literature - Czech

Anděl J., Statistické metody, MATFYZPRESS, Praha 1998.

Zvára K., Regrese, MATFZYPRESS, Praha 2008.

Last update: Antoch Jaromír, prof. RNDr., CSc. (06.02.2024)
Teaching methods -

Lecture and exercises.

Last update: Antoch Jaromír, prof. RNDr., CSc. (06.02.2024)
Requirements to the exam -

The exam consists of a written and an oral part. Solving an example is part of the written exam. The written part precedes the oral part, failure to complete it means that the entire exam is graded as failed and the oral part is no longer continued. Failure to pass the oral part means that both parts of the exam, written and oral, must be repeated at the next term. The exam grade is determined based on the evaluation of the written and oral parts.

The written part (example) corresponds to examples from topics that correspond to the syllabus of the lecture and at the same time corresponds to what was actually presented in the exercise and/or in the lecture.

The requirements for the oral part of the exam correspond to the course syllabus to the extent that was presented in the lecture.

Last update: Antoch Jaromír, prof. RNDr., CSc. (06.02.2024)
Syllabus -

1. From data to model and from model to data.

2. Random variable and its characteristics.

3. Random vectors and their characteristics.

4. Selected discrete and continuous distributions.

5. Normal distribution and distributions derived from it.

6. Limit laws of probability theory and their use in statistics.

7. Principles of estimation theory.

8. Point and interval estimates.

9. Principles of hypothesis testing.

10. U-test, t-test, F-test and their ordinal variants.

11. Goodness-of-fit tests.

12. One, two and multiple choice problem.

13. From data to model revisited.

14. Graphical representation of data, descriptive statistics.

15. Regression analysis.

16. Contingency tables.

17. Selected statistical procedures.

18. Bayesian approach to data analysis.

Last update: Antoch Jaromír, prof. RNDr., CSc. (06.02.2024)