Bi6050 Introduction to Biostatistics in English

Přírodovědecká fakulta
jaro 2019
Rozsah
0/2/0. 2 kr. (příf plus uk plus > 4). Ukončení: zk.
Vyučující
doc. RNDr. Jakub Těšitel, Ph.D. (cvičící)
Garance
doc. RNDr. Jakub Těšitel, Ph.D.
Ústav botaniky a zoologie – Biologická sekce – Přírodovědecká fakulta
Kontaktní osoba: doc. RNDr. Jakub Těšitel, Ph.D.
Dodavatelské pracoviště: Ústav botaniky a zoologie – Biologická sekce – Přírodovědecká fakulta
Rozvrh
Po 18. 2. až Pá 17. 5. Po 9:00–10:50 B09/316
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
Cíle předmětu
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. 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.
Osnova
  • 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
Literatura
  • 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
Výukové metody
Weekly practicals composed of a short theoretical introduction to discussed topics followed by computations in R.
Metody hodnocení
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.
Vyučovací jazyk
Angličtina
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Předmět je zařazen také v obdobích jaro 2018, jaro 2020, jaro 2021, jaro 2022, jaro 2023, jaro 2024, jaro 2025.