Mathematics, Statistics 2

Enumerative combinatorics: Volumes, tuples, counting options with standard formulas. The basic probability theory: Basic concepts, elementary probability calculations, decision trees. Inferential statistics: random variables and probabilities, types of random variables, the simple rules of probability theory, probability, density and distribution function, moments of distributions (median, mode, mean, variance and standard deviation), the general rules of the probability theory with distributions, special distributions and their moments (binomial, Poisson, normal and lognormal distribution, two-dimensional distributions), the general rules of the probability theory with two-dimensional distributions, covariance and correlation, linear transformations of random variables, correlation between normal and lognormal distribution, properties of estimators and selected estimators, hypothesis tests, selected tests.

Mode of delivery

face to face



Recommended or required reading and other learning resources/tools

Alt, Raimund (2012): Mathematik. Eine Einführung für Wirtschaftswissenschaftler, LINDE Verlag. Raimund Alt (2010): Statistik. Eine Einführung für Wirtschaftswissenschafter, LINDE Verlag.

Planned learning activities and teaching methods

Lecture, exercises with practical examples

Assessment methods and criteria

in accordance with module exam regulations

Prerequisites and co-requisites

Mathematics and Statistics 1


Degree programme

Logistics & Transport Management (Bachelor)



ECTS Credits


Language of instruction




Academic year



2 SS



Learning outcome

The students will have the ability to graphically present and analyze data and frequency distributions. The students will also have the ability to calculate and interpret the relevant location, dispersion parameters and statistical correlation. They will also have the expertise to conduct a regression and analyze the results. Furthermore, the students will be in the position to be able to summarize the most important rules of the probability theory, the moments and relevant distributions and apply them appropriately. The students will acquire the knowledge of the structure of estimation or testing process and have the ability to do a computer based application on selected cases. Students should be able to present, with the aid of real examples, improvements based on real examples and computer aided visualization.

Course code