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Lecturer(s)
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Course content
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The course progresses gradually from basic theoretical concepts to the demonstration of specific methods for practical analytical task. Practical exercises apply theoretical knowledge to real-world problems. For greater clarity, problems are selected from the field of geography and related disciplines, which are familiar to geoinformatics students. The practical exercises are carried out primarily in the R language, or alternatively in MS Excel. 1. Basic concepts, sample surveys 2. Random variable, descriptive statistics 3. Probability distribution, distribution function 4. Parameter estimation, hypothesis testing 5. Statistical tests 6. ANOVA, contingency tables 7. Correlation and regression analysis 8. Exploratory analysis, data visualization
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Laboratory Work
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Learning outcomes
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The aim is to introduce students to the basics methods of statistics for the needs of data processing in geoinformatics during their studies, but also for applicability in real practice.
The course focuses on the acquisition of knowledge. Upon completion of the course, students will be able to define key concepts, describe and appropriately select the main methods of data processing, and demonstrate both theoretical knowledge and practical skills in solving model problems.
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Prerequisites
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Application of knowledge from previous studies (the VYMET course).
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Assessment methods and criteria
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Oral exam
The student has to prove the knowledge of the subject topics. The ability to use the R scripting language in practice.
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Recommended literature
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Brázdil, R. et al. (1995). Statistické metody v geografii. Brno.
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Gareth, J. et al. An Introduction to Statistical Learning. New York. 2021.
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Hendl, J. (2004). Přehled statistických metod zpracování dat: analýza a metaanalýza dat. Praha.
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Zvára, K., Štěpán, J. (2019). Pravděpodobnost a matematická statistika. Praha.
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