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Lecturer(s)
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Dvořáčková Olga, Mgr. Ph.D.
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Course content
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Covered topics: 1. Descriptive statistics (incl. graphical representation) of various data types 2. Principles of hypothesis testing. Statistical significance 3. Chi-square test 4. Contingency tables 5. t-tests 6. ANOVA 7. Nonparametric tests 8. Correlation and regression Selected topics can be discussed more thoroughly depending on the students' needs.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Demonstration, E-learning, Individual tutoring
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Learning outcomes
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The course is refreshing the previously acquired statistical knowledge. It focuses predominantly on the techniques of data evaluation which are needed for a bachelor thesis.
Students will be able to analyze data and present their findings in a format suitable for the bachelor thesis.
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Prerequisites
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Students are assumed to have a basic knowledge of MS Excel or its alternatives. Having at least an approximate idea of the thesis theme is advantageous.
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Assessment methods and criteria
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Oral examination, Written examination, Analysis of student's work activities (technical works)
Students ought to process the supplied data using any software available (MS Excel, IBM SPSS). Students should actively participate in the class.
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Recommended literature
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FIELD A. Discovering Statistics Using SPSS. 3. ed. London: Sage Publications, 2009. ISBN 978-1-84787-906-6.
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