Day 1: Introduction to R (working in the batch modus, programming language R, reading and displaying data, writing functions, simulating data) + Basic theory (Probability distributions, Central limit theorem, Bayes theorem, Bootstrapping, Inference from data using frequentist and Bayesian methods, classical frequentist tests (t-, F-, Chi-, Wilcoxon-test)) Day 2: Computation techniques (Monte Carlo simulation, Approximations), Application to own or simulated data: Comparison of two means using frequentist and Bayesian methods, Discussion
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