12954 - Biostatistics Modulübersicht

Module Number: 12954
Module Title:Biostatistics
  Biostatistik
Department: Faculty 2 - Environment and Natural Sciences
Responsible Staff Member:
  • Prof. Dr. rer. nat. Birkhofer, Klaus
Language of Teaching / Examination:English
Duration:1 semester
Frequency of Offer: Every summer semester
Credits: 6
Learning Outcome:The Module Biostatistics provides rigorous introduction to data analysis for the applied sciences, especially for ecology, with a particular focus on R programming (R software).
Contents:Part "Experimental design”

The correct experimental design is the foundation of a good quality research. Students will learn about basic types of experimental designs, about their benefits and limits:
  • Random sampling
  • Non-random sampling (block design, longitudinal data, latin square, split plot)
  • Pseudoreplication
Part "Descriptive statistic" 

The use of descriptive statistics allows to understand quantitative insights across a large data set. Students will 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" 
The student will have substantial theoretical knowledge of basic statistical analyses and associated inference and evaluation methods. Students will 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, U de Mann-Witney and Kruskal-Wallis).
  • General and generalized linear models.
  • Introduction to mixed models.
Part "Multivariate analysis" 

The student can learn the statistical technique used to analyse data that arises from more than one variable. Students will learn about:
  • Principal component analysis (PCA)
  • Non metric multidimensional analysis (NMDS)
  • Redundancy analysis (RDA)
  • Canonical Correspondence Analysis (CCA)
Part "Representation of results: graphs and tables" 

Basic concepts for a proper presentation of the results for journal publication.


Part "Introduction to R program" 

The course will be taught using the R program. R is a powerful software system designed for analysing, and graphing data. R is an integrated programming environment, allowing users to script their own functions. Students will 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.
Recommended Prerequisites:
  • Module 41102 Ecology
  • Module 41217 General and Applied Ecology
Mandatory Prerequisites:None
Forms of Teaching and Proportion:
  • Lecture / 2 Hours per Week per Semester
  • Exercise / 2 Hours per Week per Semester
  • Self organised studies / 120 Hours
Teaching Materials and Literature:None
Module Examination:Final Module Examination (MAP)
Assessment Mode for Module Examination:Written examination, 90 min.

In case of regular (documented) attendance in the exercises, additional 10 % as a bonus is possible.
Evaluation of Module Examination:Performance Verification – graded
Limited Number of Participants:None
Part of the Study Programme:
  • Abschluss im Ausland / Environmental and Resource Management / keine PO
Remarks:No offer in summer semester 2024!
Complementary module for study programme Bachelor and Master Environmental and Resource Management.
Every student has to bring his own laptop!

The lectures take place as a block course at the end of the lecture period.
Module Components:
  • 240782 Lecture/Exercise Biostatistics 
  • 240784 Examination Biostatistics
Components to be offered in the Current Semester:
  • no assignment