## Bi6050 Introduction to Biostatistics in English

Faculty of Science
Spring 2023
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Jakub Těšitel, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Jakub Těšitel, Ph.D.
Department of Botany and Zoology - Biology Section - Faculty of Science
Contact Person: doc. RNDr. Jakub Těšitel, Ph.D.
Supplier department: Department of Botany and Zoology - Biology Section - Faculty of Science
Timetable
Tue 11:00–12:50 B09/316
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to introduce the principles of statistical thinking and the use of statistics in science. Special emphasis is put on the practical use of statistical analyses and presentation of the results.
Learning outcomes
After completing the course, the students will be able to process their own data and apply basic statistical methods to test hypotheses related to their research. They will also be aware about the assumptions and limitations of the basic statistical methods used in the course.
Syllabus
• 1. Introduction to statistics – types of data, population and sample, basic descriptive statistics; introduction to R and the R Studio environment – the console, importing data, R project, data frames and vectors, indexing, basic descriptive statistics
• 2. Probability and likelihood – random variables, probability distribution and density, normal distribution, likelihood; Plotting in R – histograms, boxplots, barplots, computing probabilities, quantiles and generating random samples of normal distribution.
• 3. Efficient workflow of graph production in R 1 – Vector and raster graphics, exporting graphs from R to other software, plot options and adjustments, adding objects to plots, colors and symbols.
• 4. Efficient workflow of graph production in R 2. Legends, graph item descriptions, multi-panel plots, margins.
• 5. Hypothesis testing – the principle, type I and II errors, goodness-of-fit test, Chi-square distribution; Computation of goodness-of-fit test and probabilities from Chi-square distribution.
• 6. Contingency tables – basic analysis by goodness-of-fit test, odds and odds ratios, coincidence vs. causality and experimental vs. observational approach.
• 7. T-distribution, confidence intervals, t-tests (two-sample, single-sample, paired).
• 8. F-distribution, F-test, analysis of variance, post-hoc multiple comparisons, analysis of residuals.
• 9. Data transformation and non-parametric methods, permutation tests.
• 10. Linear regression, Pearson correlation, Spearman non-parametric correlation, scatterplots
• 11. Multiple regression and linear models, model selection, additivity and interaction
Literature
recommended literature
• Maindonald JH. 2008. Using R for Data Analysis and Graphics: Introduction, Code and Commentary. Available from: ftp://cran.r-project.org/pub/R/doc/contrib/usingR.pdf
• Webpages: R for cats (https://rforcats.net/), Quick-R (http://www.statmethods.net/)
• CRAWLEY, Michael J. The R book. 2nd ed. Chichester: Wiley, 2013. xxiv, 1051. ISBN 9780470973929. info
• R graphs cookbookdetailed hands-on recipes for creating the most useful types of graphs in R-- starting from the simplest versions to more advanced applications. Edited by Hrishi V. Mittal. Birmingham, U.K.: Packt Open Source, 2011. iv, 255 p. ISBN 9781849513074. info
Teaching methods
Weekly practicals composed of a short theoretical introduction to discussed topics followed by computations in R.
Assessment methods
Essay structured as a research paper based on statistical analysis of students’ real or generated data. Presentation of the essays at a “mini-conference” held in the exam period.
Language of instruction
English