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2022S, VO, 2.0h, 3.0EC

## Properties

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

## Learning outcomes

After successful completion of the course, students are able to understand and apply the most important statistical methods required for the analysis of experimental data: graphical representation, computation of characteristic numbers, estimation of unknown parameters, testing of hypotheses, fitting of linear regression models. The students can select and apply appropriate methods for specific areas of application.

## 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. Parametric tests: How do I test whether my data show significant deviations from theory? ? 5. Linear regression: Is there a correlation between two or more observed quantities, and how is it quantified?

## Teaching methods

Practical 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.

## Mode of examination

Written

The course takes place in HS4 for the time being. When restrictions to 50% have to be imposed, I will switch to online mode.

## Course dates

DayTimeDateLocationDescription
Thu08:00 - 10:0003.03.2022 - 23.06.2022FH Hörsaal 4 Statistics
Mon15:00 - 16:0029.08.2022Sem.R. DA grün 06A Prüfungseinsicht zur Prüfung am 25.8.2022
Statistics - Single appointments
DayDateTimeLocationDescription
Thu03.03.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu10.03.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu17.03.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu24.03.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu31.03.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu07.04.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu14.04.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu21.04.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu28.04.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu05.05.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu12.05.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu19.05.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu26.05.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu02.06.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu09.06.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu16.06.202208:00 - 10:00FH Hörsaal 4 Statistics
Thu23.06.202208:00 - 10:00FH Hörsaal 4 Statistics
Mon29.08.202215:00 - 16:00Sem.R. DA grün 06A Prüfungseinsicht zur Prüfung am 25.8.2022

## Examination modalities

Written examination. Pocket calculator and tables are required. A formula collection of up to 8 pages A4 is allowed.

## Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Thu09:00 - 11:0029.06.2023FH Hörsaal 3 - MATH written30.05.2023 09:00 - 21.06.2023 18:00TISS1. Prüfung 2023S

## Course registration

Begin End Deregistration end
07.02.2022 09:00 07.03.2022 18:00 08.03.2022 09:00

## Literature

The slides and the handout (2 or 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-eboo

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

Elementary Differential and integral calculus, basic linear algebra.

German