Programme info

Micro-credential: Biostatistics

Study load: 7 ECTS credits
Language of instruction: English
Location: Campus Drie Eiken
Faculty: Faculty of Science

Course content

The biostatistics course focuses on the application of different techniques which biologists often need for their research. The following topics are covered: one-way and two-way analysis of variance, mixed models, regression, multiple regression, analysis of covariance, generalized mixed models (Binomial and Poisson), experimental design, multivariate statistics (principal component analysis, discriminant analysis, correspondence analysis), mixtures, survival analysis and the basic principles of Bayesian statistics.

Learning outcomes

This micro-credential focuses on the following learning outcomes.

1. The participant is able to translate a biological question into a statistical hypothesis.

2. The participant can select the appropriate technique to test this hypothesis.

3. The participant can perform this technique in the program RStudio and the participant can create simple reports in the package RMarkdown.

4. The participant can interpret the outcome and formulate a conclusion in biological terms.

5. The participant has obtained a thorough understanding of the following methods: general and generalized linear models, mixed models, mixtures, survival analysis, principal component analysis, linear discriminant analysis, correspondence analysis and the basics of Bayesian statistics.

Practical organisation

Class contact teaching

• Lectures

• Practice sessions

• 1or 2 separate contact moments for participants who follow this course as a microcredential – timing is in agreement with participants

Personal work

• Exercises

Directed self-study

Lectures: recording available via video link on Blackboard

Evaluation

Written examination without oral presentation
• Open book
• Multiple-choice
• Open-question
• Exercises

Assessment criteria
The exam consists of a series of specific questions and multiple choice questions with respect to 6 datasets and associated statistical analyses. For 4 of them, the output of the analysis is presented and specific questions need to be answered. For the 2 other cases, the analyses need to be performed in R as well and the output needs to be interpreted. The level of difficulty is comparable to that during the practicals. Students are allowed to consult the course notes, solutions to the exercises, and other books if deemed helpful. No corrections for guessing will be applied.