142.090 Statistics
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2017S, VO, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VO Lecture

Aim of course

The aim is to make the students familiar with the most important statistical methods that are employed in the analysis of experimental data. An essential part of the lecture is the demonstration of the methods on real or simulated data sets that are representative for the experimental situation. All algorithms are implemented in Matlab and will be given to the students along with the data sets.

Subject of course

1. Descriptive statistics: How do I present my data in a concise, but meaningful way? 2. Stochastic modeling: How do I construct a model of my data that correctly describes the random aspects of an experiment, and which models are relevant in the experimenter's practice? 3. Parametric estimation, confidence intervals: How do I estimate physical quantities from my data, and how do I asses the uncertainty of the estimates? 4. Linear regression: Is there a correlation between two or more observed quantities, and how is it quantified? 5. Modelling of background, robust methods: How do I separate the signal from the experimental background, and how can I minimize the influence of the background? 6. Parametric and non-parametric tests: How do I test whether my data show significant deviations from theory? 7. Simulation: Why should I simulate my experiment and how can I do it?



Course dates

Thu08:15 - 10:0002.03.2017 - 22.06.2017FH Hörsaal 2 Lecture
Mon08:15 - 10:0026.06.2017FH Hörsaal 2 Additional lecture
Statistics - Single appointments
Thu02.03.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu16.03.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu23.03.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu30.03.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu06.04.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu27.04.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu04.05.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu11.05.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu18.05.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu01.06.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu08.06.201708:15 - 10:00FH Hörsaal 2 Lecture
Thu22.06.201708:15 - 10:00FH Hörsaal 2 Lecture
Mon26.06.201708:15 - 10:00FH Hörsaal 2 Additional lecture

Examination modalities

Written examination. A formula collection of up to 8 pages is allowed. 


DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Tue10:00 - 12:0002.07.2024Sem.R. DA grün 05 written03.06.2024 09:00 - 26.06.2024 12:00TISS1. Prüfung 2024S
Thu10:00 - 12:0004.07.2024FH Hörsaal 6 - TPH written03.06.2024 09:00 - 01.07.2024 12:00TISS2. Prüfung 2024S
Fri10:00 - 12:0027.09.2024FH Hörsaal 3 - MATH written02.09.2024 09:00 - 23.09.2024 12:00TISS3. Prüfung 2024S
Thu10:00 - 12:0007.11.2024Sem.R. DA grün 05 written11.10.2024 09:00 - 03.11.2024 18:00TISS4. Prüfung 2024S
Fri10:00 - 12:0028.02.2025FH Hörsaal 5 - TPH written31.01.2025 09:00 - 21.02.2025 18:00TISS5. und letzte Prüfung 2024S

Course registration

Not necessary


Study CodeObligationSemesterPrecon.Info
033 261 Technical Physics Not specified
066 460 Physical Energy and Measurement Engineering Mandatory2. Semester



The slides and the handout (4 slides per page) can be downloaded by the students.

The course is also based on my ebook  "Wahrscheinlichkeitsrechnung und Statistik: Für Studierende der Physik" (in German). It can be downloaded free of charge from:


For the exam you will also need the tables.

Further recommended books:

L. Lyons, A practical guide to data analysis for physical science students, Cambridge University Press, 1991.

L. Lyons, Statistics for Nuclear and Particle Physicists, Cambridge University Press, 1986.

W. Stahel, Statistische Datenanalyse: Eine Einführung für Naturwissenschaftler, Vieweg+Teubner, 2007.

V. Blobel und E. Lohrmann, Statistische und numerische Methoden der Datenanalyse, Teubner, 1998. L. Fahrmeir et al., Statistik: Der Weg zur Datenanalyse, Springer, 2007.

S. M. Ross, Statistik für Ingenieure und Naturwissenschaftler, Spektrum, 2006.

Previous knowledge

Knowledge of Matlab helpful, but not required