# 142.090 Statistics This course is in all assigned curricula part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_21",{id:"j_id_21",showEffect:"fade",hideEffect:"fade",target:"isAllSteop"});});This course is in at least 1 assigned curriculum part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_23",{id:"j_id_23",showEffect:"fade",hideEffect:"fade",target:"isAnySteop"});}); 2024S 2023S 2022S 2021S 2020S 2019S 2018S 2017S 2016S 2015S 2014S 2013S 2012S 2011S 2010S

2020S, VO, 2.0h, 3.0EC

## Properties

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

## 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 lecture starts at 8:15!

## Course dates

DayTimeDateLocationDescription
Thu08:15 - 10:0005.03.2020 - 12.03.2020FH Hörsaal 2 Lecture
Statistics - Single appointments
DayDateTimeLocationDescription
Thu05.03.202008:15 - 10:00FH Hörsaal 2 Lecture
Thu12.03.202008:15 - 10:00FH Hörsaal 2 Lecture

## 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
Mon10:00 - 12:0001.07.2024Sem.R. DA grün 05 written03.06.2024 09:00 - 26.06.2024 12:00TISS1. Prüfung 2024S
Thu10:00 - 12:0004.07.2024FH Hörsaal 3 - MATH 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

Not necessary

## Curricula

Study CodeObligationSemesterPrecon.Info
033 261 Technical Physics Not specified
066 460 Physical Energy and Measurement Engineering Mandatory2. Semester
066 461 Technical Physics Mandatory2. Semester

## 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

Differential and integral calculus, basic linear algebra. Knowledge of Matlab helpful, but not required

German