Subjects

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Multivariate Statistical Methods - AP402014

Title:	Multivariate Statistical Methods
Guaranteed by:	Department of Analytical Chemistry (402)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2019
Semester:	both
Points:	0
E-Credits:	0
Examination process:
Hours per week, examination:	2/1, other [HT]
Capacity:	winter:unknown / unknown (unknown) summer:unknown / unknown (unknown)
Min. number of students:	unlimited
State of the course:	taught
Language:	English
Teaching methods:	full-time
Teaching methods:	full-time
Level:
Note:	course is intended for doctoral students only can be fulfilled in the future you can enroll for the course in winter and in summer semester

Guarantor:	Matějka Pavel prof. Dr. RNDr. Člupek Martin Ing. Ph.D.
Classification:	Mathematics > Probability and Statistics
Interchangeability :	P402014

Examination dates

Annotation -

This course dedicated to Ph.D. students is focused on various multivariate chemometric methods including classification and regression. The practical application of multivariate statistical techniques is emphasized to solve specific analytical problems related to the interests of course attendees. The preprocessing of experimental data before the statistical evaluation is discussed considering the reliability of the results obtained and the elimination of interfering effects, e.g. noise, background deformation, instrumental function.

Last update: Matějka Pavel (08.06.2019)

Aim of the course -

Students will be able to:

describe the principles of individual multivariate statistical methods

describe the algorithms used in the implementation of these methods in commercial software

apply these statistical methods for the solution of physicochemical and analytical issues

Last update: Pátková Vlasta (16.11.2018)

Literature -

R) Jambu, Michel. (1991). Exploratory and Multivariate Data Analysis. Elsevier. Retrieved from https://app.knovel.com/hotlink/toc/id:kpEMDA0002/exploratory-multivariate/exploratory-multivariate

R) Matthias Otto (2016). Chemometrics: Statistics and Computer Application in Analytical Chemistry, Third Edition. 2017 Wiley‐VCH Verlag. Print ISBN:9783527340972 |Online ISBN:9783527699377 |DOI:10.1002/9783527699377 https://onlinelibrary.wiley.com/doi/book/10.1002/9783527699377

A) Esbernsen K.H., Swarbrick B (2018): Multivariate Data Analysis (6th Edition), Camo Software AS

A) Adams M.J. (2004): Chemometrics in Analytical Spectroscopy (2nd Edition), The Royal Society of Chemistry

A) Andrade-Garda J.M. (Ed) (2013): Basic Chemometric Techniques in Atomic Spectroscopy (2nd Edition), The Royal Society of Chemistry

A) Published review papers and original studies.

Last update: Pátková Vlasta (16.11.2018)

Syllabus -

1. Basic concepts of multivariate statistical methods and source data

2. Methods of source data processing - correction of noise and other parasitic signals, data aggregation, normalization algorithms

3. Fundamental statistical processing of multidimensional data

4. Multidimensional analysis of variance, estimations and hypothesis testing

5. Methods of exploratory analysis with focus on the principal components analysis (PCA)

6. Multivariate classification methods including cluster analysis

7. Multivariate regression methods

8. Critical analysis and review of a given/selected published paper dealing with the application of multivariate statistical methods

9. Seminar - presentation of reviews of published papers

10. Assignment of projects - use of own data of the participating group of Ph.D. students - independent processing of data by properly selected multivariate methods

11. Seminar - presentation of project outputs and their discussion

Last update: Matějka Pavel (16.05.2019)

Learning resources -

electronic materials available on-li in the domain vscht.cz

Last update: Pátková Vlasta (16.11.2018)