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Statistics for Data Analysis


  • The course is usually offered in winter term.
  • In addition to the lecture, practices are offered during which problems from the lectures are being discussed and solved.
  • Please use KLIPS2.0 for finding information on dates of lectures and practices.

Contents and objectives of the module

Students learn advanced, specialized methods in the field of statistics.
Real-world issues and challenges are analyzed, and findings and research results of specialized theories/ methods are evaluated and discussed. Students develop working processes for real-world problems and challenges.

  • Probability theory: distributions, (conditional) density functions.
  • Linear (multiple) regression, conditional expected value function.
  • Assumptions, model selection, hypothesis testing
  • Maximum likelihood
  • Time series