Module MA2600-KP04, MA2600

Duration

1 Semester

Turnus of offer

each summer semester

Credit points

4

Course of studies, specific fields and terms:

• Master Medical Informatics 2019, optional subject, Medical Data Science / Artificial Intelligence
• Master Biophysics 2019, optional subject, Elective
• Master Medical Informatics 2014, optional subject, ehealth / infomatics
• Master Computer Science 2012, optional subject, specialization field medical informatics
• Master Computer Science 2012, optional subject, specialization field bioinformatics
• Master Computer Science 2012, optional subject, advanced curriculum stochastics
• Bachelor CLS 2010, compulsory, mathematics

Classes and lectures:

• Biostatistics 2 (exercise, 1 SWS)
• Biostatistics 2 (lecture, 2 SWS)

Workload:

• 35 hours private studies
• 15 hours exam preparation
• 25 hours programming
• 45 hours in-classroom work

Contents of teaching:

• Knowledge of model assumptions and mathematical foundation of model assumptions for the linear model
• Knowledge of possible sources of errors in the modelling
• Competence in independent analysis of a study using the linear model
• Competence in correctly interpreting study results
• Competence in parameter interpretation and regression diagnostics
• Knowledge of model assumptions and mathematical foundation of the generalized linear model
• Competence in the independent analysis of a simple study with a dichotomous outcome
• Competence in correctly interpreting study results of a study with a dichotomous outcome

Qualification-goals/Competencies:

• The students are able to enumerate and explain the assumptions of the classical linear model.
• The students are able to describe typical applications of the classical linear model.
• The students are able to list the differences between the linear model and the logistic regression model.
• The students are able to describe possible error sources in modelling the linear model.
• The students are able to calculate the estimators (point and interval estimators, residual) in the linear model by hand.
• The students are able to evaluate the graphics for regression diagnostics in the linear model.
• The students are able to interpret the results of studies, where a linear, a logistic or a Cox regression model was applied.
• The students are able to draw and interpret Kaplan-Meier curves.
• The students are able to perform data transformations.

Grading through:

• written exam

Is requisite for:

• Multivariate Statistics (MA4944)
• Interdisciplinary Seminar (MA3300)

Requires:

• Biostatistics 1 (MA1600-KP04, MA1600, MA1600-MML)

Responsible for this module:

• Prof. Dr. rer. biol. hum. Inke König

Teacher:

• Institute of Medical Biometry and Statistics
• Prof. Dr. rer. biol. hum. Inke König

Literature:

• Ludwig Fahrmeir, Thomas Kneib, Stefan Lang : Regression: Modelle, Methoden und Anwendungen ISBN-13 9783540339328
• Dobson, Annette J & Barnett, Adrian : An Introduction to Generalized Linear Models, 3rd ed. Chapman & Hall/CRC: Boca Raton (FL), 2008

Language:

• offered only in German

Notes:

Last Updated:

18.02.2026