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Please note the lectures are given in Czech language only.
Introduction to data analysis and experimental design for ecologists. Topics: principles of parameter estimation and inference in statistics, analysis of variance, simple one-way ANOVA, two-way ANOVA, multiple comparison tests, linear regression analysis, assumptions of the linear model, data transformation, regression × correlation, frequency tables, spatial and temporal autocorrelation (introduction), principles of design of ecological experiments, factorial and orthogonal designs, block designs. Detailed information available in Moodle system under MB120P31 Biostatistika a plánování ekologických pokusů (old course code; in Czech only). Last update: Štefánek Michal, Mgr. (29.04.2022)
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Recommended textbooks: Lepš J.: Biostatistika. - Skripta BF JčU. Sokal a Rohlf: Biometry. - W.H. Freeman, San Francisco. Crawley M.J. (2013): The R book. - 2nd ed. John Wiley & Sons, Chichester. Dalgaard P. Introductory Statistics with R. - Springer. Last update: Štefánek Michal, Mgr. (29.04.2022)
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Written test, testing theoretical knowledge (8 open questions - 4 points each). At least 50% points are required to pass the exam. The exam can be entered only after passing of the practicals.
Practicals consist of analysing datasets by introduced statistical methods. This is achieved in the form of three tests (to be elaborated partly at home and partly during lessons of practicals). At least 65% of points must be obtained from these tests in total. More information on Moodle subject. Alternatives under CoViD 19 measures
The tests at praciticals and exam shall be transferred into Moodle environment. Other conditions remain the same.
Last update: Štefánek Michal, Mgr. (29.04.2022)
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Syllabus of course topics in Biostatistics and planning of ecological experiments
1) Introduction to statistics - variable properties, descriptive statistics 2) Introduction to statistics - variance, relations of two variables, correlation 3) Basics of statistical inference, estimation of basic population parameters 4) Basics of statistical modeling, regression, model diagnostics 5) Formulation and testing of hypotheses, analysis of variance 6) Bayesian statistics, differences between statistical interaction and predictor correlation 7) ANOVA - continuation; Design of experiments 8) Design of experiments; Factors with fixed and random effects 9) Partial, nonlinear and local regression 10) Multiple regression - continuation, model selection, use of information criteria 11) Categorical dependent variables, classical tests 12) Introduction to logistic regression and parametric analysis of non-normal variables 13) "Consultation lecture" Last update: Štefánek Michal, Mgr. (29.04.2022)
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