SubjectsSubjects(version: 949)
Course, academic year 2021/2022
Multivariate data analysis - N413040
Title: Mnohorozměrná analýza dat
Guaranteed by: Department of Mathematics (413)
Faculty: Faculty of Chemical Engineering
Actual: from 2019
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: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Is provided by: M413004
For type:  
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Šnupárková Jana RNDr. Ph.D.
Zikmundová Markéta Mgr. Ph.D.
Kříž Pavel Ing. Mgr. Ph.D.
Is interchangeable with: M413004, AM413004
Examination dates   Schedule   
Annotation -
Last update: Kříž Pavel Ing. Mgr. Ph.D. (17.05.2016)
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: Kříž Pavel Ing. Mgr. Ph.D. (17.05.2016)

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. (17.05.2016)

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

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

Härdle W. K., Simar L.: Applied Multivariate Statistical Analysis, Springer 2012.

Rencher A. C., Christensen W. F.: Methods of Multivariate Analysis, John Wiley & Sons 2002.

Varmuza K., Filzmoser P.: Introduction to Multivariate Statistical Analysis in Chemometrics, CRC Press 2009.

Learning resources -
Last update: Kříž Pavel Ing. Mgr. Ph.D. (17.05.2016)

Lecture notes on e-learning

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

Teaching methods -
Last update: Kříž Pavel Ing. Mgr. Ph.D. (17.05.2016)

Lectures and seminars.

Syllabus -
Last update: Kříž Pavel Ing. Mgr. Ph.D. (18.10.2016)

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.

Registration requirements -
Last update: Kříž Pavel Ing. Mgr. Ph.D. (17.05.2016)

Basic knowledge of probability theory and statistics (corresponding to the content of the course Applied statistics (N413004) or Statistical data analysis (N143042)).

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