Bi1122c Statistical analysis of experimental data in R - practical course

Faculty of Science
Autumn 2025
Extent and Intensity
0/3/0. 3 credit(s). Type of Completion: z (credit).
In-person direct teaching
Teacher(s)
Mgr. Petra Ovesná, Ph.D. (lecturer)
Guaranteed by
Mgr. Petra Ovesná, Ph.D.
Department of Experimental Biology – Biology Section – Faculty of Science
Contact Person: Mgr. Petra Ovesná, Ph.D.
Supplier department: Department of Experimental Biology – Biology Section – Faculty of Science
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the practical exercises is to learn how to effectively use the R software to calculate statistical tests and models appropriate to a given experimental design, and to interpret the output of the models correctly.
Learning outcomes
Passing through this practical training, students should be able to: - devise the adequate design of experiment; - select appropriate statistical method for given biological experimental data and design; - analyze these data using R software; - present obtained result using reports, graphs and tables.
Syllabus
  • - Data organization for statistical analysis in R.
  • - Data import from spreadsheets. Variable types, statistical distributions, quantiles.
  • - Hypotheses testing, null and alternative hypothesis, I. and II. error type.
  • - General linear model – applications, assumption checking, interpretation by type of explanatory variable.
  • - Multiple linear model with fixed effects, interactions, model simplification.
  • - Mixed-effects models for correlated data.
  • - Generalized linear models with Gamma distribution.
  • - Generalized linear models with Poisson distribution.
  • - Logistic regression, odds ratio.
  • - Experimental design, selecting of appropriate statistical method.
Literature
  • LEPŠ, Jan. Biostatistika. Vyd. 1. České Budějovice: Jihočeská universita, 1996, 165 s. ISBN 8070401540. info
  • SOKAL, Robert R. and F. James ROHLF. Biometry : the principles and practice of statistics in biological research. 3rd ed. New York: W.H. Freeman and Company, 1995, xix, 887. ISBN 0716724111. info
  • PEKÁR, Stanislav and Marek BRABEC. Moderní analýza biologických dat 1 - 1. díl. Zobecněné lineární modely v prostředí R. 2. přepracované vydání. Brno: Masarykova univerzita, 2020, 278 pp. ISBN 978-80-210-9622-6. info
  • PEKÁR, Stanislav and Marek BRABEC. Moderní analýza biologických dat 2. Lineární modely s korelacemi v prostředí R (Modern Analysis of Biological Data 2. Linear Models with Correlations in R). 1st ed. Brno: Masarykova universita, 2012, 256 pp. ISBN 978-80-210-5812-5. info
Teaching methods
Practical lectures focused on theoretical aspects as well as practical applications. Practices in a computer room focused on training of regression modelling in R software.
Assessment methods
Knowledge will be evaluated using model examples calculated by students during the whole semester.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught every week.
Information on course enrolment limitations: Na předmět se vztahuje povinnost registrace; bez registrace může být znemožněn zápis předmětu!
The course is also listed under the following terms Autumn 2024.
  • Enrolment Statistics (recent)
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