E8600c Multivariate Methods - practices

Faculty of Science
Spring 2025
Extent and Intensity
0/1/0. 1 credit(s). Type of Completion: z (credit).
In-person direct teaching
Teacher(s)
RNDr. Eva Koriťáková, Ph.D. (seminar tutor)
RNDr. Jiří Jarkovský, Ph.D. (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
Prerequisites
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.
    • Syllabus
    • 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)
    Literature
    • 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
    Czech
    Further Comments
    The course is taught annually.
    The course is taught: every week.
    The course is also listed under the following terms Autumn 2022, Autumn 2023, Autumn 2024.
    • Enrolment Statistics (recent)
    • Permalink: https://is.muni.cz/course/sci/spring2025/E8600c