Applied Statistics

Postgraduate course

Course description

Objectives and Content

Main aim will be to provide a good overview of classical, but also more advanced statistical methods for analyzing data of different structure. Focus lies on understanding the principle behind a method and its limits, applying it via the open source software R (, and interpreting the results. The statistical methods analyzed may include one-/two-factor ANOVA, linear & non-linear least squares regression, analysis of covariance (ANCOVA), non-parametric techniques, generalized linear models, time series analysis, generalized least squares, mixed effects models, survival analysis, factor analysis, PCA, PLS, and hidden Markov models. The beginning of the lecture is dedicated to an introduction to R, thus no previous experience with the program is required.

Learning Outcomes

The course gives an overview over statistical methods that are much used in various disciplines. At the same time it gives the students a basis for understanding the ideas behind the methods and for using the methods in a rational way by means of statistical software.

Semester of Instruction

Recommended Previous Knowledge
Compulsory Assignments and Attendance
Forms of Assessment
Written examination: 4 hours.
Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Assessment Semester
Examination autumn semster only for students with leve of absence.
Examination Support Material
Examination support materials: Non- programmable calculator, according to model listed in faculty regulations