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)
Mgr. Lucie Kubínová (seminar tutor)
Mgr. Eva Budinská, Ph.D. (seminar tutor)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX - Faculty of Science
Contact Person: RNDr. Eva Koriťáková, Ph.D.
Supplier department: RECETOX - Faculty of Science
- Mon 19. 9. to Sun 18. 12. Mon 15:00–16:50 F01B1/709
- 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 multivariate data analysis of the students. 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 and based on solving real problems and examples using advanced multivariate methods. The examples will be followed by illustrative visualizations using software 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.