SubjectsSubjects(version: 954)
Course, academic year 2019/2020
Multivariate Statistical Methods - N402039
Title: Vícerozměrné statistické metody
Guaranteed by: Department of Analytical Chemistry (402)
Faculty: Faculty of Chemical Engineering
Actual: from 2013 to 2020
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
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Additional information: http://Ing. Člupek, Ph.D.
Guarantor: Matějka Pavel prof. Dr. RNDr.
Is interchangeable with: M402016
Examination dates   Schedule   
Annotation -
This course represent a continuation to the course Chemometrics in Analytical Chemistry. Various multivariate chemometric methods are introduced showing the differences among exploratory, classification and regression algorithms. The practical applications are emphasised solving specific analytical issues. The preprocessing data treatment is introduced demonstrating the importance of proper combination of individual steps for the achievement of reliable results.
Last update: VED402 (16.12.2013)
Aim of the course -

Students will be able to:

Describe fundamental concepts of multivariate statistics.

Prepare experimental multidimensional data for their statistical processing and evaluation.

Explain the principles of multivariate chemometric methods, including exploratory, classification and regression approaches and to use them for specific analytical and spectroscopic measurements.

Last update: VED402 (16.12.2013)
Literature -

A:Meloun M., Militký J., Hill M: Počítačová analýza vícerozměrných dat v příkladech, Academia Praha 2005, ISBN 80-200-1335-0

Last update: VED402 (01.10.2013)
Learning resources -

electronic materials - available in the domain

Last update: Matějka Pavel (31.08.2013)
Syllabus -

1. Data vector, data matrix and fundamental operation

2. Multivariate statistical distributions, probability distributions

3. Data corrections - methods of noise suppression, baseline corrections, multiplicative scatter correction

4. Data matrix processing and data treatment

5. Estimations and hypothesis testing

6. Multivariate analýza rozptylu

7. Exploratory data analysis

8. Principal component analysis

9. Discrimination analysis

10. Cluster analysis

11. Fuzzy cluster analysis

12. Partial least squares approach

13. Canonic correlation

14. Multivariate regression methods

Last update: Matějka Pavel (30.08.2013)
Registration requirements -


Last update: Matějka Pavel (31.08.2013)
Teaching methods
Activity Credits Hours
Konzultace s vyučujícími 0.7 20
Obhajoba individuálního projektu 0.5 14
Účast na přednáškách 1 28
Práce na individuálním projektu 0.5 14
Příprava na zkoušku a její absolvování 1.3 36
Účast na seminářích 1 28
5 / 5 140 / 140
Coursework assessment
Form Significance
Regular attendance 5
Defense of an individual project 15
Examination test 40
Continuous assessment of study performance and course -credit tests 20
Oral examination 20