CC 304: Basics of Statistics

Contents
The lecture presents an introduction to probability calculus and inductive statistics. Fundamental terms and the basics of probability calculus will be treated. This includes the terms probability, conditional probability, independence and the Bayes’ theorem. To the central terms of statistical modeling belong random variables and their allocative function, the general description of discrete and continuous allocation as well as the term expectation. The allocation of two-dimensional random vectors will be discussed and important limit statements for large samples presented. In the course of the statistical part of the lecture, it will be introduced into the basic concepts of the theory of estimation and test theory. This includes the conception behind point and interval estimator as well as significance tests and p-values. Important classical parameter testings will be presented: this includes in particular the one- and two-sample location test, Gauß’ test, t-test and Binomial test. Theoretic and practical aspects in the simple linear regression model will be discussed.

Learning outcomes
Students know basic probabilistic and statistical terms, e.g. expectation, coefficient of correlation, confidence interval, significance test and p-value. They can link the terms and know legalities which are important for their application. They are able to calculate probabilities and statistical values of specified allocations, to solve combinatorial problems and to understand simple derivations of general statements of random variables. With a problem, they can detect the relevant statements, select a solution method and apply it. The students are able to interpret and evaluate the results of statistical methods. Within the frame of a simple statistical problem, they are able to select an adequate test and apply it.

Necessary prerequisites
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Recommended prerequisites
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Examiner Performing lecturer

Dr. Ingo Steinke Dr. Ingo Steinke