t.BA.XXM5.STS.19HS (Stochastics and Statistics) 
Module: Stochastics and Statistics
This information was generated on: 29 July 2021
Stochastics and Statistics
Organised by


Version: 2.0 start 01 August 2021

Short description

Introduction to the theory of probability and statistics.

Module coordinator

Tom Weinmann (weto)

Learning objectives (competencies)

Objectives Competences Taxonomy levels
Students are familiar with the basic terms and concepts of the theory of probability and are able to create and analyze probabilistic models. F,M 3,4,5,6
Students are able to use probabilistic methods for the analytical as well as numerical calculation of probabilities. F,M 3,6
Students understand the concept of random variables and the properties of the probability density function and the distribution function. F,M 3,4
Students are familiar with the most important distributions and understand the concept of the joint distribution, the conditional distribution as well as the concept of covariance and correlation of random variables. F,M 3,5
Students understand the laws of large numbers and the central limit theorem and grasp their impact on statistical applications. F,M 3,5
Students are familiar with the most important methods for estimating parameters and testing hypotheses and are capable to apply these methods. F,M 3,4

Module contents

Basic terms:
  - probability spaces
  - independence of events
  - combinatorics and probability
  - probability of unions
Conditional probability:
  - multiplication rule
  - rule of total probability
  - Bayes' theorem
Discrete random variables:
  - distribution of a random variable
  - expected value of a random variable
  - variance and standard deviation of a random variable
  - some discrete distributions (binomial, multinomial, poisson, ...)
General random variables:
 - expected value and variance of absolutely continuous random variables
 - Some continuous distributions (uniform distribution, exponential distribution, normal distribution, ...)
 - transformations of random variables
 - joint distribution, marginal distribution and conditional distribution
 - sums of independent random variables
 - covariance, variance and correlation
Limit theorems:
 - laws of large numbers
 - central limit theorem
Statistical concepts:
 - point estimates (method of moments, maximum likelihood method)
 - interval estimates (expected value of a normal distribution with known / unknown variance, expected value of any distribution for large samples, ...)
 - testing hypotheses (binary hypotheses, parametrized hypotheses, hypotheses about the distribution function, ...)

Teaching materials

Depending on the lecturer: script, slides, exercise series

Supplementary literature




Teaching language

(X) German ( ) English

Part of International Profile

( ) Yes (X) No

Module structure

Type 3a
  For more details please click on this link: T_CL_Modulauspraegungen_SM2025


Description Type Form Scope Grade Weighting
Graded assignments during teaching semester depends on lecturer     Grade 40 Percent
End-of-semester exam exam written 90 Minutes Grade 60 Percent



Legal basis

The module description is part of the legal basis in addition to the general academic regulations. It is binding. During the first week of the semester a written and communicated supplement can specify the module description in more detail.


Course: Stochastik und Statistik - Praktikum
Stochastik und Statistik - Praktikum


  • No module description is available in the system for the cut-off date of 01 August 2099.
Course: Stochastik und Statistik - Vorlesung
Stochastik und Statistik - Vorlesung


  • No module description is available in the system for the cut-off date of 01 August 2099.