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Lecturer(s)
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Friedecký David, prof. RNDr. Ph.D.
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Course content
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Students will learn the correct procedure for planning an experiment, what design to choose, how to calculate the power of the study, descriptive statistics (location characteristics - means, medians, quantiles; data distribution) and basic visualization of descriptive data (scatter plot, boxplot). Students will learn to use data transformation to achieve normality of data. A separate chapter will then cover how to identify outliers and how to approach them. In the next section, they will be introduced to regression analysis, correlations and their interpretation. Hypothesis testing is the next chapter where both parametric and non-parametric approaches will be presented. All approaches will be illustrated with practical examples from laboratory practice. Critical points and limitations of each statistical test will be discussed in detail. The students will be intensively involved in the learning by practical testing of each statistical method directly on their own laptops. The course will include the processing of their own data using the acquired knowledge and their subsequent presentation to other students. In addition, each student will then present one selected research paper and discuss in detail the statistical methods used and critically evaluate the outputs and conclusions and point out errors. MS Excel, Statistica and GraphPad Prism software will be used to work with the data.
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Learning activities and teaching methods
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Dialogic Lecture (Discussion, Dialog, Brainstorming), Group work
- Homework for Teaching
- 5 hours per semester
- Attendace
- 26 hours per semester
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Learning outcomes
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The subject line "Basic statistical data processing" will focus on the theoretical understanding of basic statistical terms and statistical methods used in biological, chemical and medical sciences.
Students will understand the basic principles and procedures of statistical data processing.
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Prerequisites
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Students have a basic understanding of statistics acquired during their undergraduate studies.
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Assessment methods and criteria
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Student performance
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Recommended literature
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Bernd Mayer. (2011). Bioinformatics for Omics Data: Methods and Protocols.
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Dharmaraja Selvamuthu, Dipayan Das. (2018). Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control.
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Hans-Michael Kaltenbach. (2021). Statistical Design and Analysis of Biological Experiments.
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Shuzhao Li. (2020). Computational Methods and Data Analysis for Metabolomics.
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