Bi8600c Multivariate Methods - practices

Faculty of Science
Autumn 2016
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.
The course is also listed under the following terms Autumn 2014, Autumn 2015, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.
  • Enrolment Statistics (Autumn 2016, recent)
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