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Course, academic year 2023/2024
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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 2023
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
Teaching methods: full-time
Additional information: https://www.karlin.mff.cuni.cz/~maciak/nmst551_2324.php
Guarantor: doc. RNDr. Matúš Maciak, Ph.D.
doc. RNDr. Arnošt Komárek, 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 -
Last update: T_KPMS (07.05.2015)
Independent analysis of a real data set, scientific report writing.
Aim of the course -
Last update: T_KPMS (07.05.2015)

Practice in analysis of real data and scientific report writing.

Course completion requirements - Czech
Last update: RNDr. Jitka Zichová, Dr. (23.04.2018)

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

Charakter zápočtu neumožňuje opravné termíny.

Literature -
Last update: doc. RNDr. Matúš Maciak, Ph.D. (16.10.2023)

Depending on problems to be solved. More information on the seminar web site https://www.karlin.mff.cuni.cz/~maciak/nmst551_2324.php.

Teaching methods -
Last update: T_KPMS (16.05.2013)

Seminar.

Syllabus -
Last update: T_KPMS (16.09.2014)

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.

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

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.

 
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