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Lecturer(s)
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Navrátil Josef, doc. RNDr. Ph.D.
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Course content
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1. Statistical reasoning - summary of previously acquired knowledge 2. Hypothesis testing, work in STATISTICS 3. Research plans - theory and preparation of research plan 4. ANOVA 1: ANOVA: One-way, factorial, main effect 5. ANOVA 2: Nested ANOVA and RM-ANOVA 6. Regression of GLM and GLZ 7. Repetition of one-dimensional methods (Midterm test, part of the credit score) 8. Cluster analysis 9. Indirect ordination analysis 10. Direct ordination analysis 11. Discriminant analysis 12. Other statistical software - examples of data entry principles and calculations in CANOCO, B-Veagna, R 13. Credit
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Demonstration, Laboratory
- Preparation for credit
- 30 hours per semester
- Preparation for exam
- 50 hours per semester
- Class attendance
- 42 hours per semester
- Preparation for classes
- 28 hours per semester
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Learning outcomes
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The aim of the course is to acquaint students with non-elementary principles of quantitative data evaluation. More complicated concepts of one-dimensional data analysis and multidimensional data analysis will be introduced. Analytical procedures will be practiced using STATISTICA software.
Students are able to prepare an experiment to collect quantitative data, evaluate complex experimental and non-experimental research plans using GLM / GLZ methods and some multidimensional methods such as PCA, FA, MDS, cluster analysis, etc.
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Prerequisites
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To complete the course, it is necessary to complete the basic statistics course, ie familiarity with probability theory, knowledge of calculations and the importance of data characteristics, understanding the principles of hypothesis testing, correlation and linear regression.
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Assessment methods and criteria
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Written examination
Credit requirements: 1. Proof of student card (= index). 2. Personal participation in seminars, two absences are allowed (does not apply to KS). 3. Credit test passed at least 60% in total. The credit form in the standard form consists of two parts - 6 tasks in the so-called Midterm exam and 4 tasks in the credit week (for KS both in the term agreed for the 1st consultation). Both tests are electronic, software STATISTICA). The subject of the credit is the practical ability to solve statistical problems vSTATISTICA mainly including: import of data from XLS / XLSX format, data transformation, variable addition, graph adjustment to the required format, t-test calculations, F-test, one-factor ANOVA (including post-hoc test) - and its non-parametric periods, correlation (Pearson, Spearman), linear regression, analysis of variance (factorial ANOVA, RMANOVA, MANOVA, Nested design ANOVA, ANCOVA), regression models (normal, binomial, multinomic, ordinal), cluster analyzes (K- means hierarchical) and ordination analyzes (PCA, RDA, DCA / CA, CCA, CCorA). Note for PS: If you did not pass the "midterm exam" due to your absence, then you will pass it together with the 2nd credit test in the credit week. If you do not get at least 60% in the sum of the "midterm exam" and the 2nd credit test, you must pass both tests together in the corrected term - you have two corrective terms (applies to PS and KS). Exam requirements: 1. Pass a test at least 60%. The exam is focused on theoretical knowledge, it asks 14 questions. 10 questions always have four possible answers (just one of them is correct) - the body gets a point for the correct answer, the body is not deducted for incorrect answers, 4 questions are with a free answer, a point is awarded for the correct answer. The optional written part of the exam is followed by an optional oral part. 2. Demonstration of student ID (= index).
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Recommended literature
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Lepš J., Šmilauer P. Biostatistika. Episteme, České Budějovice, 2016. ISBN 978-80-7394-587-9.
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Lepš, J.et Šmilauer, P. Mnohorozměrná analýza ekologických dat. České Budějovice, 2000.
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