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Lecturer(s)
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Rost Michael, doc. Ing. Ph.D.
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Course content
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Contents of blocks of lectures and exercises: 1. Introduction to the course, organization of study, requirements for the student. 2. Statistical software for multidimensional data analysis. 3. Multifactor ANOVA (concept of interaction, hierarchical ANOVA). 4. Regression and correlation analysis (statistical dependence, correlation, correlation field, nature and derivation of OLS). 5. Multiple regression analysis, diagnostic model diagnostics, prediction, confidence intervals for prediction and reliability. 6. Introduction to nonlinear regression analysis. 7. Introduction to multidimensional statistics, matrix algebra. 8. Principial component analysis (PCA). 9. Coefficients of similarity, coefficients of dissimilarity, metrics (work with binary and categorical data). 10. Hierarchical algorithms of cluster analysis. 11. Non-hierarchical cluster analysis algorithms. 12.-14. - Classification methods
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Learning activities and teaching methods
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Skills training
- Preparation for credit
- 30 hours per semester
- Preparation for classes
- 50 hours per semester
- Class attendance
- 50 hours per semester
- Preparation for exam
- 20 hours per semester
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Learning outcomes
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The aim of the course is to deepen students' knowledge in processing, evaluating and interpreting information obtained by measurement or observation through selected biostatistical methods.
This subject has no prerequisites.
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Prerequisites
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Students understand the principles of multidimensional statistical methods. They are able to perform data processing and interpret some multidimensional methods.
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Assessment methods and criteria
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Combined exam
Active participation on lessons and exercises, or participation in seminars. Fulfillment of assigned tasks according to the teacher's instructions.
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Recommended literature
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ANDĚL, J.:. Statistické metody. Praha: Matfyzpress, 1993.
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Lepš, J., Šmilauer, P. Biostatistika. Episteme, České Budějovice, 2016. ISBN 978-80-7394-587-9.. ISBN 978-80-7394-587-9.
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Pekár, S., Brabec, M. Moderní analýza biologických dat Zobecněné lineární modely v prostředí R, Scientia, Praha 2009.
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Zar, J.H. Biostatistical analysis, 5th edition. Pearson, San Francisco, 2010.
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