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.

2018S, VO, 2.0h, 3.0EC

Properties

  • 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?

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Thu08:15 - 10:0008.03.2018 - 21.06.2018FH Hörsaal 2 Lecture
Mon10:00 - 12:0018.06.2018FH Hörsaal 3 - MATH Prüfungsvorbereitung
Mon08:15 - 10:0025.06.2018FH Hörsaal 2 Additional lecture
Fri09:00 - 09:3006.07.2018FH Hörsaal 2 Statistik SS2018 Prüfungseinsicht
Statistics - Single appointments
DayDateTimeLocationDescription
Thu08.03.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu15.03.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu22.03.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu12.04.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu19.04.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu26.04.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu03.05.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu17.05.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu24.05.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu07.06.201808:15 - 10:00FH Hörsaal 2 Lecture
Thu14.06.201808:15 - 10:00FH Hörsaal 2 Lecture
Mon18.06.201810:00 - 12:00FH Hörsaal 3 - MATH Prüfungsvorbereitung
Thu21.06.201808:15 - 10:00FH Hörsaal 2 Lecture
Mon25.06.201808:15 - 10:00FH Hörsaal 2 Additional lecture
Fri06.07.201809:00 - 09:30FH Hörsaal 2 Statistik SS2018 Prüfungseinsicht

Examination modalities

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

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Fri10:00 - 12:0030.09.2022Hörsaal 6 - RPL written05.09.2022 09:00 - 25.09.2022 09:00TISS4. Prüfung 2022S
Wed10:00 - 12:0001.02.2023Sem.R. DA grün 03 B written02.12.2022 09:00 - 27.01.2023 12:00TISS5. Prüfung 2022S
Thu09:00 - 11:0029.06.2023FH Hörsaal 4 written30.05.2023 09:00 - 21.06.2023 18:00TISS1. Prüfung 2023S

Course registration

Not necessary

Curricula

Literature

 

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:

http://bookboon.com/de/wahrscheinlichkeitsrechnung-und-statistik-ebook

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

Language

German