4879 Statistics
ao.Univ.Prof. Dr. Klaus Pötzelberger
Weekly hours
Language of instruction
04/22/19 to 05/02/19
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Day Date Time Room
Tuesday 05/07/19 10:00 AM - 12:00 PM TC.4.01
Wednesday 05/08/19 10:30 AM - 12:30 PM TC.3.01
Tuesday 05/14/19 10:00 AM - 12:00 PM TC.2.01
Wednesday 05/15/19 10:00 AM - 12:00 PM D5.0.001
Tuesday 05/21/19 10:00 AM - 12:00 PM TC.5.15
Wednesday 05/22/19 10:30 AM - 12:30 PM TC.5.01
Tuesday 05/28/19 10:00 AM - 12:00 PM TC.4.03
Wednesday 05/29/19 10:00 AM - 12:00 PM D3.0.225
Tuesday 06/04/19 10:00 AM - 12:00 PM D4.0.022
Wednesday 06/12/19 10:00 AM - 12:00 PM TC.5.27
Wednesday 06/12/19 12:00 PM - 02:00 PM TC.5.27
Tuesday 06/18/19 10:00 AM - 12:00 PM D4.0.022


 Exploratory Data Analysis

  • Location, Scale, Skewness, kurtosis estimators
  • Visualisation
  • Applied Data Analysis using R

 Statistical Inference

  • Point estimation (ML estimation, Bayesian estimation; Computing estimators in R; Evaluating estimators)
  • Hypothesis testing (Defining and evaluating tests; p-values)
  • Interval estimation (Defining and evaluating interval estimators)
  • Asymptotic evaluations (Consistency and efficiency)
  • Properties of Estimators (sufficiency, likelihood principle, Bayesian inference)

 Applications in Statistical Modelling

  • Assumptions of Regression, Gauss-Markov theorem
  • Linear regression 
  • Analysis of variance (ANOVA) models

Learning outcomes

After completing this course the student will have the ability to:

  • Describe, explain, and work with the basic concepts and definitions of statistical inference, in particular exploratory data analysis, estimation and hypothesis testing.
  • Understand how statistical inferential methods are formulated and evaluated.
  • Solve simple real-world problems where skills from statistical modelling and inferential methods are required.

Attendance requirements

Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

Teaching/learning method(s)

The course is taught as a lecture accompanied by practical examples, simulation studies and homework assignments. The lectures are aimed at providing the methodological framework, while the examples, simulation studies, and homework assignments will help students to consolidate and further expand their knowledge of the underlying ideas. Solutions to the home assignments will be discussed in class.  Active participation in class activities is an essential part of the course.


  •  20% weekly tutorials
  •  35% project in applied statistics
  •  45% final exam

      The final exam cannot be retaken

Prerequisites for participation and waiting lists

Successful completion of the courses Analysis and Linear Algebra as well as Probability within the Specialization in Business Mathematics
(Spezialisierung Wirtschaftsmathematik)


1 Author: Venables, W. and Ripley, B.
Title: Modern Applied Statistics with S

Publisher: Springer
Edition: 2nd Edition
Year: 2002
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book

Recommended previous knowledge and skills

Availability of lecturer(s)
Last edited: 2018-11-07