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Course, academic year 2024/2025
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Statistical Project Seminar - NMST551
Title: Statistický projektový seminář
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2024
Semester: winter
E-Credits: 5
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
Additional information: https://www.karlin.mff.cuni.cz/~maciak/nmst551_2425.php
Guarantor: prof. RNDr. Arnošt Komárek, Ph.D.
doc. RNDr. Matúš Maciak, Ph.D.
Teacher(s): doc. RNDr. Zdeněk Hlávka, Ph.D.
doc. RNDr. Matúš Maciak, Ph.D.
Class: M Mgr. PMSE
M Mgr. PMSE > Povinně volitelné
Classification: Mathematics > Probability and Statistics
Pre-requisite : NMST412, NMST422
Is interchangeable with: NSTP106
Annotation -
Independent analysis of a real data set, scientific report writing.
Last update: T_KPMS (07.05.2015)
Aim of the course -

Practice in analysis of real data and scientific report writing.

Last update: T_KPMS (07.05.2015)
Course completion requirements - Czech

Požadavky k zápočtu: Každý týden odevzdávat práci podle zadaného úkolu, koncem semestru odevzdat uspokojivou výzkumnou zprávu, zpracovat oponenturu.

Charakter zápočtu neumožňuje opravné termíny.

Last update: Zichová Jitka, RNDr., Dr. (23.04.2018)
Literature -

Depending on problems to be solved. More information on the seminar web site https://www.karlin.mff.cuni.cz/~maciak/nmst551_2425.php.

Last update: Maciak Matúš, doc. RNDr., Ph.D. (02.10.2024)
Teaching methods -

Seminar.

Last update: T_KPMS (16.05.2013)
Syllabus -

Statistical approach to real-life problém solving. Independent analysis of a real data set, scientific report writing. Emphasis is put on the following topics:

1. Processing of data before analysis.

2. Suitable choice of a statistical model

3. Formulation of the objectives.

4. Conduct of the analysis.

5. Correct interpretation of the results.

6. Creation of a comprehensible, objective and well formatted scientific report.

Last update: T_KPMS (16.09.2014)
Entry requirements -

This course assumes good knowledge of theoretical foundations and practical applications of linear regression, logistic regression, loglinear models, GEE, and linear mixed effects models. Programming skills with R statistical software and LaTeX document processing system are also beneficial.

Last update: Kulich Michal, doc. Mgr., Ph.D. (25.05.2018)
 
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