SubjectsSubjects(version: 949)
Course, academic year 2021/2022
Multivariate data analysis - M413004
Title: Mnohorozměrná analýza dat
Guaranteed by: Department of Mathematics, Informatics and Cybernetics (446)
Faculty: Faculty of Chemical Engineering
Actual: from 2021 to 2022
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
For type: Master's (post-Bachelor)
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Mareš Jan doc. Ing. Ph.D.
Kříž Pavel Ing. Mgr. Ph.D.
Zikmundová Markéta Mgr. Ph.D.
Interchangeability : AM413004, N413040
Is interchangeable with: AM413004
This subject contains the following additional online materials
Annotation -
Last update: Pátková Vlasta (09.01.2018)
Basic principles of selected statistical methods for analysing multidimensional data will be outlined with focus on reconciliation of the assumptions of the methods and interpretation of their results. Students will learn how to perform corresponding calculations in statistical software R.
Aim of the course -
Last update: Pátková Vlasta (09.01.2018)

Students will know:

1. Understand basic principles of selected statistical methods for multivariate data analysis

2. Reconcile assumptions of particular methods.

3. Understand the results of the methods.

4. Perform essential calculations with specific data in specialized software (R).

Literature -
Last update: Kříž Pavel Ing. Mgr. Ph.D. (05.11.2018)

R: Meloun M., Militký J., Hill M.: Počítačová analýza vícerozměrných dat v příkladech, Academia, Praha 2005.

R: Härdle W. K., Simar L.: Applied Multivariate Statistical Analysis, Springer 2015.

R: Haruštiaková D. a kol.: Vícerozměrné statistické metody v biologii, Akademické nakladatelství CERM, Brno 2012. (

A: Hendl J.: Přehled statistických metod, Portál, Praha 2012.

A: Rencher A. C., Christensen W. F.: Methods of Multivariate Analysis, John Wiley & Sons 2012.

A: Varmuza K., Filzmoser P.: Introduction to Multivariate Statistical Analysis in Chemometrics, CRC Press 2016.

A: Králová H.: Vybrané moderní metody mnohorozměrné statistické analýzy, UP v Olomouci (diploma thesis), 2013. (

Learning resources -
Last update: Pátková Vlasta (09.01.2018)

Lecture notes on e-learning

Statistická analysa dat v R (lecture notes by Doc. Spiwok, VSCHT)

Teaching methods -
Last update: Pátková Vlasta (09.01.2018)

Lectures and seminars.

Syllabus -
Last update: Pátková Vlasta (09.01.2018)

1. Data vector, data matrix and matrix algebra (multiplication, inverse matrix, eigenvalues and eigenvectors), covariance matrix.

2. Vizualisation of multidimensional data.

3. Exploratory data analysis (EDA).

4. Cluster analysis.

5. Principal component analysis (PCA).

6. Multidimensional scaling.

7. Parameter estimation and hypothesis testing. Bayesian statistics.

8. Multivariate analysis of variance (MANOVA).

9. Regression methods 1 - multiple linear regression.

10. Regression methods 2 - principal component regression (PCR), generalized linear models (GLM).

11. Discriminant analysis.

12. Canonical correlation analysis.

13. Factor analysis (FA).

14. Supplements and summary of multivariate statistical methods, buffer for holidays.

Entry requirements -
Last update: Borská Lucie RNDr. Ph.D. (13.05.2019)

Students are expected to have either completed at least one of the prerequisite courses Applied Statistics or Statistical Data Analysis or possess the equivalent knowledge on probability theory and statistics prior to enrolling in the course.

Registration requirements -
Last update: Borská Lucie RNDr. Ph.D. (06.05.2019)

No requirements.

Course completion requirements -
Last update: Kříž Pavel Ing. Mgr. Ph.D. (09.02.2018)

Credit for seminar project. Oral exam.

Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 0.5 14
Práce na individuálním projektu 1 28
Příprava na zkoušku a její absolvování 1.5 42
Účast na seminářích 1 28
5 / 5 140 / 140