After completion of the course participants will be able to:
• evaluate the assumptions of multivariate data analysis / modeling and select the appropriate method for solving a given problem,
• apply the ordination methods and cluster analysis,
• use the tools of multivariate linear and logistic regression,
• select and use generalized linear models,
• use of multivariate analysis and models in statistical software.

The subject knowledge of biostatistics (ASTA) on the Bachelor´s degree level is requested, work with PC, work with software Statistica.

Not applicable.

Not applicable.

MELOUN, Milan a Jiří MILITKÝ. Statistické zpracování experimentálních dat. 1. vyd. Praha: Plus, 1994. ISBN 80-85297-56-6. (CS)
LEGENDRE, Piere a Louis LEGENDRE. Numerical Ecology 2. vyd. Elsevier Science, 1998. ISBN 978-0444892508. (EN)
ZAR, Jerrold. Biostatistical analysis. New Jersey: Prentice Hall, 1984. ISBN 978-0321656865. (EN)
HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993. ISBN 80-200-0080-1. (CS)
ALTMAN, Douglas. Practical statistics for medical research. London: Chapman and Hall, 1991. ISBN 0412276305. (EN)

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Compliance with the requirements for terminating exercise: participation in seminars, 3 tests in more than 50% points.
Final test for more than 50% points. The test is aimed at testing the overview of multivariate statistics and stochastic modeling.

Czech

Not applicable.

1. Goals and objectives of multivariate data analysis and modeling, the relationship univariate and multivariate statistical methods
2. Multivariate statistical distribution and testing, operations with vectors and matrices
3. Similarities and distances in a multidimensional space, association matrix I
4. Similarities and distances in a multidimensional space, association matrix II
5. Cluster analysis
6. Ordination analysis - principles of dimensionality reduction
7. Ordination analysis - an overview of methods
8. Discriminant analysis
9.The principles of stochastic modeling
10. Linear models - the basics
11. Logistic regression analysis of ROC curves; Advanced prediction methods - an overview
12. Strategy multivariate analysis of clinical data, multi-dimensional data in clinical trials, bases of meta-analysis
13. Review of methods of time series analysis

The aim of the course is to provide students skills in multivariate analysis and stochastic modeling of the biological and clinical data.

Participation in seminar is mandatory, two absences are permitted. In the case of multiple absences, it is possible to substitute the seminar after the agreement with teacher (ideally in another parallel group).

26 hours, compulsory