Course: Statistical processing of environmental date

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Course title Statistical processing of environmental date
Course code KAES/SZED
Organizational form of instruction no contact
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 0
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Šlachta Martin, doc. Mgr. Ph.D.
  • Moudrý Jan, doc. Ing. Ph.D.
Course content
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 MNC). 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. Analysis of main components. 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.

Learning activities and teaching methods
Skills training, Individual preparation for exam, Individual tutoring
  • Preparation for classes - 90 hours per semester
  • Preparation for exam - 60 hours per semester
  • Preparation for credit - 50 hours per semester
  • Semestral paper - 50 hours per semester
Learning outcomes
The aim of the course is to expand and deepen students' knowledge in processing, evaluating and interpreting information obtained by measurement or observation through selected biostatistical methods.
Ability to apply knowledge and experience in further work
Prerequisites
Students understand the principles of multidimensional statistical methods and are able to perform data processing and interpret results.

Assessment methods and criteria
Student performance assessment, Combined exam

Fulfillment of assigned tasks according to the teacher's instructions.
Recommended literature
  • ANDĚL, J.:. Statistické metody.. Matfyzpress, Praha, 1993.
  • Lepš, J., Šmilauer, P. Biostatistika. Č. Budějovice, Episteme, 2016. ISBN 978-80-7394-587-9.
  • Pekár, S., Brabec, M. Moderní analýza biologických dat Zobecněné lineární modely v prostředí. Scientia, Praha, 2009.
  • Zar, J.H. Biostatistical analysis, 5th edition.. Pearson, San Francisco, 2010.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Agriculture Study plan (Version): Agricultural Ecology (1) Category: Agriculture and forestry - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Agriculture Study plan (Version): Agricultural Ecology (1) Category: Agriculture and forestry - Recommended year of study:-, Recommended semester: -