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.
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?
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.
