PřF:Bi8600 Multivariate Methods - Course Information
Bi8600 Multivariate MethodsFaculty of Science
- Extent and Intensity
- 2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Eva Budinská, Ph.D. (lecturer)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX - Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX - Faculty of Science
- Tue 12:00–14:50 D29/347-RCX2
- Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
- 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
- At the end of the course the student is able to: Prepare a dataset for multivariate analysis correctly; Describe multivariate data; Use multivariate statistical tests; Select appropriate distance or similarity metrics; Compute and visualize association matrices; Apply clustering algorithms and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Choose appropriate method for multidimensional data analysis based on advantages and limitations of the methods;Interpret results of multivariate analysis.
- Purpose and aims of multivariate data analysis – examples of multivariate data analysis, advantages and disadvantages of multivariate data analysis, data matrices, tabular and graphical visualization of multivariate data.
- Matrix operations, inverse matrix, characteristic polynomial, eigenvalues and eigenvectors, singular value decomposition (SVD)
- Multivariate distributions – random variables, descriptive statistics, confidence interval, outliers
- Multivariate statistical tests – multivariate t-test, multivariate analysis of variance
- Distance and similarity metrics in multidimensional space
- Association matrices – calculation and visualization, descriptive statistics, operations with association matrices (Mantel test, MEANSIM, ANOSIM, association matrix regression)
- Hierarchical cluster analysis – agglomerative methods, divisive methods.
- Non-hierarchical cluster analysis, identification of optimal number of clusters.
- Ordination methods – principles of data reduction, selection and extraction of variables.
- Ordination methods – principal component analysis (PCA)
- Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
- Basics of data classification, summary of methods for multivariate data analysis.
- Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
- ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
- Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
- Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
- Teaching methods
- Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
- Assessment methods
- The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
- Language of instruction
- Further Comments
- Study Materials
The course is taught annually.
- Teacher's information