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Postgraduate course

Applied Statistics

  • ECTS credits10
  • Teaching semesterSpring
  • Course codeSTAT200
  • Number of semesters1
  • LanguageEnglish
  • Resources

Main content

Teaching semester

Spring

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 (www.r-project.org), 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.

Recommended Previous Knowledge

STAT101 or STAT110

Compulsory Assignments and Attendance

Excercises

Forms of Assessment

Written examination: 4 hours.

Examination Support Material

Examination support materials: Non- programmable calculator, according to model listed in faculty regulations

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.

Exam information

Department of Mathematics