Modules

Prerequisites

Contents

• Descriptive statistics (central tendency and variation, quantiles, linear regression) • Parameter estimation (point estimates, confidence intervals, Bayesian estimastion, sufficient statistic) • Hypothesis testing (statistical tests, Neyman-Pearsonlemma, one- and two-sided tests) • Linear models (regression and variance analysis, linear classification, Gauss-Markov theorem) • Applications and use of statistical software

Learning outcomes

The students • are skilled in probability theory, • know the basic statistical models and learn how to correctly use statistical methods • can study advanced topics in stochastics • understand statistical methods thoroughly to avoid biased and false conclusions • are experienced in autonomously doing research based on specific applications.

Assessment

Prerequisite: • Successful completion of homework Final module examination: • Written examination, 90 min. OR • Oral examination, 30 min. In the first lecture it will introduced, if the examination will organized in written or oral form.

Remarks

• Study programme Mathematik B.Sc.: Compulsory elective module in complex „Specialisation“ • Study programme Wirtschaftsmathematik B.Sc.: Compulsory elective module in complex „Specialisation“ • Study programme Physics M.Sc.: Compulsory elective module in complex „Minor Subject“ • Study programme Informatik B.Sc.: Compulsory elective module in „Applied Mathematics“ or in field of application „Mathematics“ • Study programme Informatik M.Sc.: Compulsory elective module in „Mathematics“ or in field of application „Mathematics“ • Study programme Mathematics M.Sc.: Compulsory elective module in complex „Stochastics“ • Study programme Mathematical Data Science M.Sc.: Compulsory elective module in complex „Advanced Mathematical Methods in Data Science“