Inhalte

Part "Experimental design” Correct experimental design is the basis for high-quality research. Students learn about basic types of experimental designs and their advantages and limitations: • Random sampling • Non-random sampling (block design, longitudinal data, latin square, split plot) • Pseudoreplication Part "Descriptive statistic" The application of descriptive statistics allows to gain quantitative insights into large data sets. Students learn about: • Data classification: discrete (binary, nominal, ordinal) and continuous (interval, ratio) • Basic concepts of data distribution • Measures of central tendency: mean, median, or mode • Measures of variability: range, quartiles, absolute deviation, variance and standard deviation • Inferential statistics, normal and non-normal distributions and calculation of probabilities Part "Univariate analysis" Students will gain substantial theoretical knowledge of basic statistical analyses and associated inference and evaluation methods. Students learn about: • Summary of assumptions • Difference between models and statistical tests • T-test and ANOVA (Analysis of variance) • Correlation and regression analysis • Non parametric analysis (Wilcoxon, Mann-Witney-U, Kruskal-Wallis) • General and generalized linear models • Introduction to mixed models Part "Multivariate analysis" Students can learn the statistical technique for analysing data that resulting from more than one variable. Students learn about: • Principal component analysis (PCA) • Non-metric multidimensional scaling (NMDS) • Redundancy analysis (RDA) • Canonical correspondence analysis (CCA) Part "Representation of results: graphs and tables" Basics for a proper presentation of the results for publication in journals. Part "Introduction to R" The course will be taught using the R program. R is a powerful software system developed for analysing and graphically displaying data. R is an integrated programming environment, allowing users to script their own functions. Students learn about: • Comprehensive introduction to the essentials of R • Programing in R language: syntax parsing, evaluation, object-oriented programming, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code presenting the content of scientific studies

Lernziele

The Module Biostatistics provides comprehensive introduction to data analysis for the applied sciences, especially for ecology, with a particular focus on R programming (R software).

Prüfungsleistung

Written examination, 90 min. In case of regular (documented) attendance in the exercises, additional 10 % as a bonus is possible.

Literatur (7)

Gotelli, N. J. & Ellison A. M. 2013 A primer of ecological statistics. Sunderland
Dytham, C. 2011 Choosing and using statistics: a biologist's guide. Chichester
Quinn, G. P. & Keough, M. J. 2003 Experimental design and data analysis for biologists. Cambridge
Zuur, A. F., Ieno, E. N. & Smith, G. M. 2007 Analysing ecological data. New York
Dormann, C. 2020 Environmental Data Analysis: An Introduction with Examples in R. Cham
Lakicevic, M., Povak, N. & Reynolds, K. M. 2020 Introduction to R for terrestrial ecology: basics of numerical analysis, mapping, statistical tests and advanced application of R, Cham
Crawley, M. 2013 The R book. Chichester Crawley, M. 2012 Statistik mit R. Weinheim

Bemerkungen

No offer in SS 2026! All students have to bring their own laptop!