Form of learning:
After passing the course the students can conduct simple statistical analyses. They know how to calculate summary statistics and how to properly visualize data. Students are able to select suitable summary statistics and parameter estimates for different types of data sets and construct bootstrap confidence intervals for the estimated parameters. Students are able to select suitable statistical tests for different testing settings. They know how to apply different t-tests, chi-square tests and nonparametric tests and understand the general statistical assumptions that are required for applying these tests. Students are able to detect different types of dependencies between variables and they are familiar with univariate and multivariate linear regression analysis. They can conduct linear regression analysis in practice and they understand the underlying model assumptions.