SubjectsSubjects(version: 845)
Course, academic year 2018/2019
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Statistical Workshop - NMST551
Title in English: Statistický projektový seminář
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018 to 2019
Semester: winter
E-Credits: 5
Hours per week, examination: winter s.:0/2 C [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Additional information: http://www.karlin.mff.cuni.cz/~kulich/vyuka/projsem/index.html
Guarantor: doc. Mgr. Michal Kulich, Ph.D.
RNDr. Matúš Maciak, Ph.D.
Class: M Mgr. PMSE
M Mgr. PMSE > Povinně volitelné
Classification: Mathematics > Probability and Statistics
Pre-requisite : NMST432
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: T_KPMS (02.06.2016)

Depending on problems to be solved.

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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