Introduction to data analysis in ecology - MB120P188
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Introduction to data analysis and experimental design for ecologists.
Lectures take place on We 8:10 (lecture room BB), practicals on Thu 9:00 (lecture room B5). All study materials are on Moodle, course https://dl2.cuni.cz/course/view.php?id=5277. Topics covered: (*) Principles of fitting of statistical models to data and parameter estimation (*) essentials in statistical inference: confidence intervals and statistical tests (*) fitting linear models (*) guide to problems unsuitable for simple linear models The course has a lecture series and practicals in the R-environment. The practicals are based on a set of extensively commented R-scripts, which do not require any previous knowledge of R. Last update: Herben Tomáš, prof. RNDr., CSc. (31.08.2024)
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Recommended textbooks: 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. Leps J. & Smilauer P.: Biostatistics with R: An Introductory Guide for Field Biologists. Cambridge University Press 2020 Last update: Herben Tomáš, prof. RNDr., CSc. (30.08.2023)
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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 the practicals. Practicals consist of analysing datasets by introduced statistical methods. This is achieved in the form of three tests (to be done partly at home and partly during lessons of practicals). Last update: Herben Tomáš, prof. RNDr., CSc. (30.08.2023)
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Syllabus of course topics in Biostatistics and planning of ecological experiments 1) Types of variables, descriptive statistics 2) IRelations of two variables, correlation, basics of probability theory 3) Essentials of statistical inference, estimation of population parameters, confidence intervals 4) Basics of statistical modelling, fitting linear models, regression, model diagnostics 5) Formulation and testing of hypotheses, introduction to analysis of variance 6) Analysis of variance, post-hoc tests 7) ANOVA with several predictors, statistical interaction 8) Multiple regression, model selection, use of information criteria 9) Partial, nonlinear and local regression, data transformations 10) Design of experiments; hierarchical ANOVA, Factors with fixed and random effect 11) Categorical dependent variables 12) Summary, dealing with missing data, problems in data analysis Last update: Herben Tomáš, prof. RNDr., CSc. (30.08.2023)
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