Bi1122c Statistical analysis of experimental data in R - practical course

Faculty of Science
Autumn 2024
Extent and Intensity
0/3/0. 3 credit(s). Type of Completion: z (credit).
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 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
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)
Study Materials
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!
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