This course gives insight into statistical principles and methods, with an emphasis on design and analysis of epidemiological research. The course consists of various elements including lectures, exercises, an R course with a data analysis project, as well as SAS courses and tutorials.
Its content covers the basic concepts of probability theory and statistical theory, methods of data description and estimation, tests to compare observations (for quantitative, nominal and non-normal outcomes), the issue of multiple testing, concept of sample size calculation, modelling associations using Linear Regression and Generalised Linear Models. A special focus is given to the practical application of these statistical methods for the participants’ own statistical data analyses and to the presentation and interpretation of the results.
Prerequisites: Good knowledge of calculus and algebra
Type of examination: Written Exam
Course coordinator: Dr. Ursula Berger
When: Winter Semester (Oct - Dec)
How to register? Please contact the PhD program Office firstname.lastname@example.org
Lectures – Students are provided with theoretical knowledge on the various statistical methods. Epidemiological examples are provided to illustrate their application and to allow for discussions about the theories learned.
Exercises – In the weekly exercises, students apply the methods they have learned in the lectures to practical examples. They are encouraged to present and discuss their solutions.
Tutorials – Students with little background in statistics are offered additional tutorials where they can profit from the experience and knowledge of their more advanced colleagues. In the tutorials the content of the actual lecture and exercise class is revised and additional exercises are discussed.
R-course – Students learn to perform their own statistical data analyses using the statistical software R.
Data Project - A data project offers students the possibility to address scientific questions by applying the learned statistical and epidemiological methods to real world data. They perform their analyses using R and present their results in form of a short report.
SAS+ Course – Students are offered an additional SAS course, where they have the opportunity to replicate the analyses of the data project using an alternative statistical software.