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Module guide WS 2018-2022

Module MA2600 T

Module part: Biostatistics 2 (BioStat2a)

Duration:


1 Semester
Turnus of offer:


each summer semester
Credit points:


4
Course of studies, specific field and terms:
  • Master Computer Science 2019 (module part), Module part, Arbitrary semester
  • Master Computer Science 2014 (module part), Module part, Arbitrary semester
Classes and lectures:
  • Biostatistics 2 (exercise, 1 SWS)
  • Biostatistics 2 (lecture, 2 SWS)
Workload:
  • 45 Hours in-classroom work
  • 25 Hours programming
  • 15 Hours exam preparation
  • 35 Hours private studies
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:
  • exam type depends on main module
Is requisite for:
Requires:
Responsible for this module:
  • Siehe Hauptmodul
Teachers:
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:

Prerequisites for attending the module:
- None (The competences of the required modules are required for this module, but the modules are not a prerequisite for admission.)

Prerequisites for the exam:
- Preliminary examinations can be determined at the beginning of the semester. If preliminary work has been defined, it must have been completed and positively assessed before the initial examination.

Letzte Änderung:
21.1.2020