# ESF:DXH_MSTA Multivariate Statistical Analy - Course Information

## DXH_MSTA Multivariate Statistical Analysis

**Faculty of Economics and Administration**

Spring 2024

**Extent and Intensity**- 12/12/0. 12 credit(s). Type of Completion: z (credit).
**Teacher(s)**- doc. Mgr. Maria Králová, Ph.D. (lecturer)
**Guaranteed by**- doc. Mgr. Maria Králová, Ph.D.

Division of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Contact Person: Mgr. Lucie Přikrylová

Supplier department: Division of Applied Mathematics and Computer Science – Faculty of Economics and Administration **Timetable**- each odd Friday 8:00–9:50 VT202, except Fri 5. 4.
- Timetable of Seminar Groups:

*M. Králová* **Prerequisites**- The basics in the calculus of probability, statistical inference and testing procedures
**Course Enrolment Limitations**- The course is only offered to the students of the study fields the course is directly associated with.

**fields of study / plans the course is directly associated with**- there are 26 fields of study the course is directly associated with, display
**Course objectives**- 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 **Learning outcomes**- 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; **Syllabus**- 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

**Literature**- Kimmo Vehkalahti: Multivariate Analysis for the Behavioral Sciences, Second Edition, Taylor&Francis. 2019

*required literature*- WICKHAM, Hadley and 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

*recommended literature***Teaching methods**- 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/ **Assessment methods**- 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.
**Language of instruction**- English
**Further comments (probably available only in Czech)**- Study Materials

The course can also be completed outside the examination period.

The course is taught annually. **Teacher's information**- 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.

- Enrolment Statistics (recent)

- Permalink: https://is.muni.cz/course/econ/spring2024/DXH_MSTA