PřF:Bi8600c Multivariate Methods - pract. - Course Information
Bi8600c Multivariate Methods - practicesFaculty of Science
- Extent and Intensity
- 0/1/0. 1 credit(s). Type of Completion: z (credit).
- RNDr. Eva Koriťáková, Ph.D. (seminar tutor)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
Mgr. Eva Budinská, Ph.D. (seminar tutor)
Mgr. Lucie Brožová (seminar tutor)
- Guaranteed by
- RNDr. Jiří Jarkovský, Ph.D.
RECETOX - Faculty of Science
Contact Person: RNDr. Eva Koriťáková, Ph.D.
Supplier department: RECETOX - Faculty of Science
- Mon 15:00–16:50 A1/609 - IBA (A1,6.p, Kamenice 3)
- Bi8600 Multivariate Methods
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Course objectives
- The course objectives are to improve knowledge and practical skills of the students about multivariate data analysis. During the course, the students will learn methods for visualization of multivariate data, the mathematical background of multivariate methods for analysis of such data, and they will also practice interpretation of acquired results.
- Learning outcomes
- After the course, the students will be able to:
- Describe and visualize multivariate data;
- Use multivariate statistical tests correctly;
- Choose appropriate distance or similarity metrics;
- Calculate and visualize association matrices;
- Select and apply relevant clustering methods;
- Apply ordination methods on multivariate data;
- Interpret results obtained by multivariate analyses.
- 1. Description and visualization of multivariate data
- 2. Multivariate statistical tests: multivariate t-test; multivariate analysis of variance
- 3. Distance and similarity metrics in multidimensional space and their calculation
- 4. Association matrix, its calculation and use
- 5. Cluster analysis and its application in analysis of multivariate data
- 6. Ordination methods – principal component analysis (PCA)
- 7. Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
- Legendre, P., Legendre, L. (1998) Numerical Ecology. Elsevier, 2nd ed
- FLURY, B., H. RIEDWYL: Multivariate Statistics. A Practical Approach, Chapman and Hall, London — New York 1988
- Zar, J.H. (1998) Biostatistical Analysis. Prentice Hall, London. 4th ed
- THEODORIDIS, Sergios. Introduction to pattern recognition : a MATLAB approach. Amsterdam: Academic Press, 2010. x, 219. ISBN 9780123744869. info
- Teaching methods
- Teaching is interactive, online using Microsoft Teams and it is based on solving real problems and examples using advanced multivariate methods. The examples will be followed by illustrative visualizations using software STATISTICA, SPSS, Matlab, and R.
- Assessment methods
- The course is finished by credit. Submission of two homework assignments is required.
- Language of instruction
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/sci/autumn2020/Bi8600c