Bi1122 Statistical analysis of experimental data in R

Faculty of Science
Autumn 2024
Extent and Intensity
1/0/0. 1 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
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
Timetable
Thu 14:00–14:50 B09/316
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 course is to familiarize the students with the logic of biological experiments - how the setup and design of these experiments must correspond with their aims, with the usage of appropriate statistical treatment of the data and with interpretation of results.
Learning outcomes
At the end of the course the students are able to:
- set up experimental design suitable for specified purpose;
- select appropriate statistical treatment for the data and experimental design;
- test hypotheses using statistical tests or models in R software;
- generalize, interpret and present the results of statistical tests using an adequate form.
Syllabus
  • Data collecting. Organization of data for statistical analysis in R.
  • Data import from spreadsheets. Variable types, statistical distributions, quantiles, hypotheses testing, null and alteantive hypothesis, I. and II. error type.
  • Experimental design, selecting of appropriate statistical method.
  • X2 test. F-test, t-test. One-way analysis of variance, homogeneity of variances, independence of residuals, data transformations, contrasts, a priori and post-hoc tests.
  • Multiple analysis of variance: factorial, nested, and block designs, repeated measures ANOVA; interaction, fixed effects and random effects models, mixed model.
  • Covariance analysis. Correlation analysis, Pearson, Spearman and partial correlation coefficient.
  • Regression analysis, linear and non-linear regression, multiple regression.
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
  • PEKÁR, Stanislav and Marek BRABEC. Moderní analýza biologických dat. 3. díl. Nelineární modely v prostředí R (Modern Analysis of Biological Data. 3. Non-Linear Models in R). 1st ed. Brno: Masarykova univerzita, 2019, 218 pp. ISBN 978-80-210-9277-8. info
Teaching methods
Lectures focused on theoretical aspects, which will be trained in the practical course.
Assessment methods
The exam is based on the practical statistical treatment of three sets of experimental data, covering the methods presented at the course. Selection and justification of an method used is an integral part of the exam. For all calculations the R software is used.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Information on course enrolment limitations: Na předmět se vztahuje povinnost registrace; bez registrace může být znemožněn zápis předmětu!
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