DXH_MSTA Multivariate Statistical Analysis

Ekonomicko-správní fakulta
jaro 2024
Rozsah
12/12/0. 12 kr. Ukončení: z.
Vyučující
doc. Mgr. Maria Králová, Ph.D. (přednášející)
Garance
doc. Mgr. Maria Králová, Ph.D.
Oddělení aplikované matematiky a informatiky – Ekonomicko-správní fakulta
Kontaktní osoba: Mgr. Lucie Přikrylová
Dodavatelské pracoviště: Oddělení aplikované matematiky a informatiky – Ekonomicko-správní fakulta
Rozvrh
každý lichý pátek 8:00–9:50 VT202, kromě Pá 5. 4.
  • Rozvrh seminárních/paralelních skupin:
DXH_MSTA/01: každý lichý pátek 10:00–11:50 VT202, kromě Pá 5. 4., M. Králová
Předpoklady
The basics in the calculus of probability, statistical inference and testing procedures
Omezení zápisu do předmětu
Předmět je určen pouze studentům mateřských oborů.
Mateřské obory/plány
předmět má 26 mateřských oborů, zobrazit
Cíle předmětu
At the end of the course students should be able:
- to identify the multivariate character of tasks
- be well versed in advanced multivariate statistical methods
- to asses assumptions of methods concerning an application context
Výstupy z učení
After graduation of the course student should:
• be able to perform a competent selection of an appropriate method concerning particular problems;
• to perform analyses of assumptions;
• solve tasks based on real data utilising R environment - be able to interpret statistical analysis outputs;
Osnova
  • Based on students' preferences, topics from the following list will be selected:
  • Introduction to R environment
  • An Outline of Different Approaches to Ordinal Data Analysis
  • Introduction into Multivariate Analysis
  • Principal Component Analysis
  • Factor Analysis
  • Cluster Analysis
  • Discriminant Analysis
  • Higher-Order ANOVA
Literatura
    povinná literatura
  • Kimmo Vehkalahti: Multivariate Analysis for the Behavioral Sciences, Second Edition, Taylor&Francis. 2019
    doporučená literatura
  • WICKHAM, Hadley a Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. First edition. Sebastopol, CA: O'Reilly, 2016, xxv, 492. ISBN 9781491910399. info
Výukové metody
Theoretical lectures
Computer-aided seminar sessions via R environment which can be freely downloaded from http://www.r-project.org/
Further, a powerful and productive user interface for R called „RStudio“ can be consequently freely downloaded from http://www.rstudio.com/
Metody hodnocení
The progress test includes theoretical questions; however, the major part lies in data processing based on the appropriate method and its interpretation. The requirement for taking the test: to be active at compulsory seminar sessions and competed group seminar work. For Erazmus students: online participation at seminars via MS Teams, active participation at seminar work project, presence at final exam.
Vyučovací jazyk
Angličtina
Informace učitele
Data is merely the raw material of knowledge. However, the big issue is the ability of humans to use, analyse and make sense of the data. Most crucial scientific, economic, and business decisions are made based on data analysis which in its complexity can produce valuable information.
When dataset consists of units at which not just one but many (possibly correlated) variables are measured, Multivariate Statistical Analysis provides tools to get meaningful patterns and insights hidden behind the data. Consideration of statistical dependence among variables makes multivariate analysis somewhat different in approach and considerably more complex than the corresponding univariate analysis when there is only one response variable under consideration.


There are three major tasks of multivariate analysis:


• Reduction of multidimensionality (higher number of correlated variables can be represented by a smaller number of representative variables)


• Detection of outliers (with a prospect to multidimensionality which is non-intuitive)


• Looking for patterns and structures hidden in data


This course covers most of the methods used in Multivariate Statistical Analysis (see a list of topics). The aim of this course is not only to offer a theoretical background to these methods but also hands-on experience of real data processing and an interpretation of software outputs within an R environment.

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Studijní materiály
Předmět je dovoleno ukončit i mimo zkouškové období.
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích jaro 2021, jaro 2022, jaro 2023, jaro 2025.