Learning objectives
By successful participation the following competences are acquired:
The students have a good grasp of the basic methods of descriptive statistics for analysis and presentation of data. They are familar with the concept of probability and can apply probability-distributions in standard situations, where randomness plays a role. They are able to model and to solve (or simulate) application-oriented tasks.
Content
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| Descriptive Statistics; Introduction to Probability: Random experiment and random events, relative frequency, combinatorics, laplace-probability, independent events, Bayes' Theorem; Random variables: distribution function, expectation, variance; special distributions: binomial, poisson, normal, central limit theorem; Elements of inferential statistics: parameter estimation and confidence intervals; two-dimensional random variables: covariance, correlation, linear regression; simulation; Markov-Chains: simple waiting queues. |
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