PřF:Bi8600 Multivariate Statistical Meth. - Course Information
Bi8600 Multivariate Statistical 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).
- prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
- Guaranteed by
- prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX - Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
- Tue 15:00–17:50 A1/609 - IBA (A1,6.p, Kamenice 3)
- 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: Prepare correct dataset for multivariate analysis; Select appropriate distance or similarity metrics including metrics for biological communities; Apply clustering alggorithm and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Apply linear discriminant analysis and have knowledge of its principles; Have knowledge of advantages and limitations of methods of multivariate analysis; Interpret results of multivariate analysis; Have overview of available software for multivariate analysis of data.
- Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
- Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
- Distribution of multidimensional data - basic tests.
- Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
- Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
- Logistic regression - comparison with discrimination analysis.
- Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
- Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
- Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
- SAR, QSAR, QSAM.
- Multivariate analysis of variance (MANOVA).
- 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 quaetions 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