Duration:
1 Semester | Turnus of offer:
each summer semester | Credit points:
7 |
Course of studies, specific field and terms: - Bachelor CLS 2023 (compulsory), mathematics, 4th semester
- Bachelor CLS 2016 (compulsory), mathematics, 4th semester
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Classes and lectures: - Biostatistics 2 (practical course, 2 SWS)
- Biostatistics 2 (exercise, 1 SWS)
- Biostatistics 2 (lecture, 2 SWS)
| Workload: - 40 Hours private studies
- 15 Hours exam preparation
- 85 Hours programming
- 70 Hours in-classroom work
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Contents of teaching: | - Assumptions in the classical linear model
- Last squares method and geometric representation
- Stochastic properties, testing the general linear hypothesis, construction of confidence intervals and confidence ellipsoids
- Regression diagnostics and model choice
- Logistic regression: basics, model specification, threshold model, maximum likelihood estimation, tests and confidence intervals
- Survival Analysis: Kaplan-Meier curves, Log-Rank test, assumptions and parameter estimation in Cox regression
- Data structures in R, functions and functionals in R
- Statistical analysis in R: descriptive statistics (frequency tables, metrics), graphical representation, statistical tests (t-, X2-, U-, Log-Rank-), executable protocolls (literate programming) with knitr, bootstrapping, cross-validation, linear regression, logistic regression, Cox regression
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Qualification-goals/Competencies: - The students are able to enumerate and explain the assumptions of the classical linear model.
- They are able to describe typical applications of the classical linear model.
- They are able to list the differences between the linear model and the logistic regression model.
- They are able to describe possible error sources in modelling the linear model.
- They are able to calculate the estimators (point and interval estimators, residual, prediction) in the linear model by hand.
- They are able to evaluate the graphics for regression diagnostics in the linear model.
- They are able to interpret the results of studies, where a linear, a logistic or a Cox regression model was applied.
- They are able to draw and interpret Kaplan-Meier curves.
- They are able to perform data transformations.
- They are able to program their own R functions.
- They are able to present data by suitable and pleasing graphics.
- They are able to conduct linear, logistic and Cox regression analysis by means of R packages and to evaluate the results on the computer.
- They are able to execute statistical tests (t-, X2-, U-, Log-Rank-) in R, to formulate the hypotheses and to make a test decision.
- They are able to illustrate the principle of bootstrapping and cross-validation and to implement it in R.
- They are able to create a report that meets the requirements of academic work by means of the R package knitr.
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Grading through: |
Is requisite for: |
Requires: |
Responsible for this module: - Prof. Dr. rer. biol. hum. Inke König
Teachers: - Dr. rer. hum. biol. Björn-Hergen Laabs
- MitarbeiterInnen des Instituts
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Literature: - Fahrmeir, Ludwig; Kneib, Thomas; Lang, Stefan (2009): Regression: Modelle, Methoden und Anwendungen - Springer: Heidelberg
- Dobson, Annette J & Barnett, Adrian (2008): An Introduction to Generalized Linear Models, 3rd ed. - Chapman & Hall/CRC: Boca Raton
- Sachs, Lothar; Hedderich, Jürgen: Angewandte Statistik: Methodensammlung mit R - 15. Auflage, Springer: Heidelberg
- Ligges, Uwe: Programmieren mit R - 3. Auflage, Springer: Heidelberg
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Language: |
Notes:Admission requirements for taking the module:
- None (The competencies of the modules listed under 'Requires' are needed for this module, but are not a formal prerequisite)
Admission requirements for participation in module examination(s):
- Examination prerequisites can be defined at the beginning of the semester. If preliminary work is defined, it must have been completed and positively evaluated before the first examination.
Module exam(s):
- MA2600-L1: Biostatistics 2, written exam, 90 min, 100 % of module grade |
Letzte Änderung: 7.2.2023 |
für die Ukraine