Allgemeine Angaben 

Computerbased Data Analysis   
















Allocations: 1  
eLearning[Provide new moodle course in current semester] 



Angaben zur Abhaltung 

The lecture will give an introduction into the basic techniques for the analysis of experimental data. It will cover among other things the following topics:
 The scientific method  The concept of probability and its interpretations  Bayes' theorem  Random variables  Probability distributions and their moments  Important distributions: binomial, multinomial, Poisson and Gaussian distribution  Multivariate distributions  Marginal and conditional probability distributions  Covariance and correlation coefficient  Functions of (multiple) random variables  Central limit theorem  Gaussian uncertainty propagation for ndimensional functions and covariance matrix  Statistical and systematic uncertainties  Parameter estimation using the method of least squares  Estimating the goodness of fit  Parameter estimation using the (extended) maximumlikelihood method  Relation between leastsquares and maximumlikelihood method  Estimating the significance of a signal 


There are no preconditions in addition to the requirements for the Master’s program in physics. 


After successful completion of this module, students are able to
 understand and apply fundamental statistical concepts  understand and apply basic dataanalysis techniques to suitable data  apply firstorder uncertainty propagation in the most general case  estimate and correctly interpret statistical and systematic uncertainties  estimate model parameters by performing fits to (multidimensional) data  estimate the statistical significance of signals in the presence of background  (when attending the tutorials) develop tools for moderately complex dataanalysis tasks using the Python programming language 




class lecture The goal of the lecture is to provide a solid theoretical background. To this end, methods and concepts will be derived, where possible, from first principles.
In the tutorials, the concepts that are explained in the lecture will be applied to concrete examples. In small groups of students, short Python programs will be developed. The examples will be mostly from particle physics. However, the tutorials will focus mainly on the statistical aspects of the problems and are prepared such that they do not require deeper particlephysics preknowledge. 




Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende*r identifizieren. 


Zusatzinformationen 

 G. Cowan, "Statistical data analysis", Oxford University Press.  R. J. Barlow, "Statistics: A guide to the use of statistical methods in the Physical Sciences", Wiley Verlag.  S. Brandt, "Datenanalyse für Naturwissenschaftler und Ingenieure", Springer Spektrum.  B. Roe, "Probability and Statistics in Experimental Physics", Springer Verlag.  M. G. Kendall and A. Stuart, "The Advanced Theory of Statistics Vol IIII", Charles Griffin, London.  V. Blobel und E. Lohrmann, "Statistische und numerische Methoden der Datenanalyse", Teubner Studienbücher Verlag.  D. S. Sivia and J. Skilling, "Data Analysis, a Bayesian Tutorial", Oxford Science Publications.  P. R. Bevington and D. K. Robinson, "Data reduction and error analysis for the physical sciences", McGrawHill.  L. Lyons, "Statistics for nuclear and particle physics", Cambridge University Press. 




