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Course info
KMA / MSM
:
Course description
Department/Unit / Abbreviation
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KMA
/
MSM
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Multivariate Statistical Methods
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
5
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
2
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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|
|
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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11 / -
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7 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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1
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
Yes
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Fundamental theoretical course |
No
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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KIV/ADSZ, KIV/DGSM, KIV/FINS
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Histogram of students' grades over the years:
Graphic PNG
,
XLS
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Course objectives:
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The aim of this course is to introduce the basic ideas and tools of the multivariate statistical methods.
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Requirements on student
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Demonstrate knowledge and understanding of the material treated in the course, including the mathematical apparatus used. Use rigorous arguments in calculus and ability to apply them in solving problems on the topics in the syllabus.
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Content
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1. Multivariate random variable, multivariate distribution, distribution function, moments.¨
2. Multivariate normal distribution, Wishart and Hotelling distributions.
3. Descriptive statistics of multivariate data.
4. Regression analyses, general linear data model.
5. ANOVA
6. Principal components. Factor, discriminant and cluster analyses.
7. Cluster analyses
8. Factor analyses
9. Discriminant analyses.
10. Analysis of multivariate categorical data
11. Use of general and statistics programs
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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-
Basic:
Reif, Jiří. Metody matematické statistiky. Plzeň : Západočeská univerzita, 2000. ISBN 80-7082-593-6.
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Basic:
Devore, Jay L. Probability and statistics for engineering and the sciences. Boston, MA: Brooks/Cole, Cengage Learning, 2012. ISBN 978-0-538-73352-6.
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Recommended:
Rao, Radhakrishna Calyampudi. Lineární metody statistické indukce a jejich aplikace. Praha : Academia, 1978.
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Recommended:
Anděl, Jiří. Matematická statistika. Praha : SNTL, 1985.
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Recommended:
Hebák, Petr; Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
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Recommended:
Hebák, Petr. Vícerozměrné statistické metody [1]. Praha : Informatorium, 2004. ISBN 80-7333-025-3.
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Recommended:
Hebák, Petr; Hustopecký, Jiří; Malá, Iva. Vícerozměrné statistické metody [2]. Praha : Informatorium, 2005. ISBN 80-7333-036-9.
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Recommended:
Hebák, Petr. Vícerozměrné statistické metody [3]. Praha : Informatorium, 2005. ISBN 80-7333-039-3.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Contact hours
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52
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Graduate study programme term essay (40-50)
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40
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Presentation preparation (report in a foreign language) (10-15)
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15
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Preparation for an examination (30-60)
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40
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Total
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147
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
formulovat a vysvětlit definici pravděpodobnosti (v rozsahu předmětu KMA/PSA) |
popsat a vysvětlit principy statistické inference - zejména principy bodových a intervalových odhadů a principy testování statistických hypotéz (v rozsahu předmětu KMA/PSA) |
popsat a vysvětlit základní operace maticového počtu (v rozsahu předmětu KMA/LA) |
popsat a vysvětlit základní pojmy diferenciálního a integrálního počtu (v rozsahu předmětů KMA/M1 a KMA/M2) |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
odlišit různé typy náhodných veličin v (diskrétní, spojité) a různé typy rozdělení v jednorozměrném případě |
využívat znalostí základních statistických metod a postupů pro jednoduchou analýzu dat |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
popsat výhody a nevýhody multivariačních metod |
porozumět základním problémům z oblasti vícerozměrné náhodné veličiny |
znát vybrané metody vícerozměrné statistické analýzy dat |
Skills - skills resulting from the course: |
aplikovat nástroje vícerozměrné statistické analýzy na praktické úlohy |
rozpoznat, které nástroje vícerozměrné statistické analýzy jsou vhodné a potřebné pro modelování náhody ve zkoumaném problému |
uplatnit správně formální i obsahovou stránku v matematickém projevu, a to písemném i ústním |
v alespoň jednom SW prostředí implementovat vybrané nástroje multivariační analýzy |
zpracovat datové soubory standardních rozsahů a vizualizovat statistické informace o těchto datech z pohledu vícerozměrné statistické analýzy |
Competences - competences resulting from the course: |
N/A |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
Skills - skills achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
Competences - competence achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Individual study |
Practicum |
Interactive lecture |
Skills - the following training methods are used to achieve the required skills: |
Individual study |
Interactive lecture |
Practicum |
Competences - the following training methods are used to achieve the required competences: |
Individual study |
Interactive lecture |
Practicum |
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