Multivariate Statistical Methods

  • Credits: 4
  • Ending: Examination
  • Range: 2P + 2C
  • Semester: summer
  • Faculty of Economic Informatics

Teachers

Included in study programs

Information Management

Teaching results

At the end of the semester, students will have a good overview of multivariate statistical methods, which are currently widely used in various areas of economic practice, such as:
Knowledge
- Students will distinguish multivariate statistical methods in terms of their classification and will know the basic principles, starting points and conditions of use of individual multivariate statistical methods. In the final exam, students will use this knowledge to solve practical problems using the statistical software package SAS.
Skills
- Students will be able to design and identify a suitable multivariate statistical method to achieve the goal of analysis, indicating the possibilities of its further use.
Competencies
- Students will know how to: apply a suitable multivariate statistical method, verify the conditions of its use, interpret and present the results of the analysis;
- evaluate the acquired knowledge in solving real economic and social tasks in practice using the SAS system.

Indicative content

Multivariate statistical analysis is one of the most important statistical tools characterizing various phenomena. It is accompanied by wide range of methods and procedures that address multivariate problems in various respects. The course provides theoretical analysis of multivariate statistical methods, control of their basic principles, implementation of individual steps of analysis of the methods, the conditions under which individual methods are used as well as their application.

Support literature

1. VOJTKOVÁ, M. - STANKOVIČOVÁ, I.: Viacrozmerné štatistické metódy s aplikáciami v softvéri SAS. Bratislava: Letra Edu, 2020. 2. vydanie. ISBN 978-80-89962-58-7 (print), ISBN 978-80-89962-59-4 (online)
2. MELOUN, M. – MILITKÝ, J. – HILL, M: Statistická analýza vícerozměrných dat v příkladech. Praha: Karolinum, 2017. ISBN 80-200-1254-0
3. MELOUN, M. – MILITKÝ, J.: Interaktivní statistická analýza dat. Praha: Karolinum, 2012. ISBN 80-200-1254-0
4. MELOUN, M. – MILITKÝ, J.: Kompendium statistického zpracování dat. Praha: Karolinum, 2012. ISBN 80-200-1254-0
5. HEBÁK, P. - HUSTOPECKÝ, J. - JAROŠOVÁ, E. – PECÁKOVÁ, I.: Vícerozměrné statistické metódy (1). Informatorium, Praha 2004. ISBN 80-7333-025-3
6. HEBÁK, P. - HUSTOPECKÝ, J. – MALÁ, I.: Vícerozměrné statistické metódy (2). Informatorium, Praha 2005. ISBN 80-733-036-9
7. HEBÁK, P. - HUSTOPECKÝ, J. - PECÁKOVÁ, I. – PRŮŠA, M. – ŘEZÁNKOVÁ,H. – VLACH, P. – SVOBODOVÁ, A.. : Vícerozměrné statistické metódy (3). Praha: Informatorium, 2005. ISBN 80-7333-039-3
8. BAKYTOVÁ, H.- BODJANOVÁ, S.- RUBLÍKOVÁ, E.: Viacrozmerná analýza. Bratislava: ES VŠE, 1988 resp. 1991.
9. TABACHNICK, B.G. – FIDELL, L. S.: Using Multivariate statistics. 6th ed., Edinburg: Pearson Education Limited, 2014. ISBN 13: 978-1-292-02131-7
10. HAIR, J. F. - BLACK, W. C. - BABIN, B. J. - ANDERSON, R. E.: Multivariate data analysis. 7th ed. New York: Macmillan Publishing Company, 2010. ISBN 13: 978-0138132637
11. SHARMA, S.: Applied multivariate techniques. New York: John Wiley & Sons, 1996. ISBN 0-471-31064-6
12. RENCHER. A. C..: Methods of Multivariate Analysis. New York: John Willey & Sons, 1995. ISBN 0-471-57152-0
Literature will be continuously updated with the latest scientific and professional titles.

Syllabus

1. Basic concepts of multivariate analysis. 2. Methods of multicriteria evaluation. 3. Principal component analysis. 4. Factor analysis. Methods for estimating factor model parameters. 5. Rotation of factors. General scheme of application of factor analysis. 6. Comparison of factor analysis and principal component analysis. 7. Cluster analysis. Hierarchical clustering methods. 8. Non-hierarchical clustering methods. Interpretation of clusters. 9. Canonical correlation analysis. 10. Discriminant analysis. Analytical task of discriminant analysis. 11. Classification task of discriminant analysis. Verification of classification accuracy. 12. Logistic regression. 13. Summary of lectured topics.

Requirements to complete the course

40 % semester project processed in SAS Enterprise Guide
60 % final exam

Student workload

Total study load (in hours): 156 hours
Distribution of study load
Lectures participation: 26 hours
Seminar participation: 26 hours
Preparation for seminars: 13 hours
Elaboration of Semester project: 26 hours
Presentation of Semester project: 13 hours
Preparation for final exam: 52 hours

Language whose command is required to complete the course

Slovak

Date of approval: 11.03.2024

Date of the latest change: 30.03.2022