FX003 Experiment setup and interpretation

Faculty of Science
Autumn 2020
Extent and Intensity
1/1. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
Teacher(s)
Mgr. Filip Münz, PhD. (lecturer)
Mgr. Filip Münz, PhD. (seminar tutor)
Guaranteed by
prof. RNDr. Josef Humlíček, CSc.
Department of Condensed Matter Physics - Physics Section - Faculty of Science
Contact Person: Mgr. Filip Münz, PhD.
Supplier department: Department of Condensed Matter Physics - Physics Section - Faculty of Science
Prerequisites
F7270 Math. methods for data anal.
Prerequisites for this course are general knowledge of probability and statistics (to the extent of the course F7270): manipulation with random variates, overview of basic discrete and continuous distributions, theory of estimates and hypothesis testing, linear and non-linear regression analysis.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
Students should be (after finishing the course) capable of: - analysing statisticaly of (inhomogeneous) data sample - designing a set of proposed measurements, reveal critical points in the space of possible entry parameters - choosing and applying of a robust method of data reduction - eliminating (disentangling) trends in series of data - choosing of a suitable optimization process in model-experiment comparison
Learning outcomes
Students should be (upon finishing the course) capable of:
- analysing statisticaly of (inhomogeneous) data sample
- selecting of a parametric or non-parametric model function suitable for the problem in question
- designing a set of proposed measurements, reveal critical points in the space of possible entry parameters
- choosing and applying of a robust method of data reduction
- eliminating (disentangling) trends in series of data
- choosing of a suitable optimization process in model-experiment comparison
- provide correct confidence intervals for parameters even under some physical constraints
Syllabus
  • Review of mathematical statistics
  • Easy cases - physical experiments revisited
  • Regression - from linear to orthogonal, robust methods
  • Multidimensional problems, reducing number of variables
  • Event densities, KDE
  • Moment estimates from samples
  • Orthogonal spaces (polynoms)
  • Upper limits
Literature
    recommended literature
  • Statistical methods in experimental physics. Edited by Frederick E. James. 2nd ed. Hackensack, N.J.: World Scientific, 2006. xviii, 345. ISBN 9789812705273. info
Teaching methods
Lectures combined with practical examples (during exercises), independently solved problems (sumulated "experimental" data).
Assessment methods
During the course, students solve individually 1-2 problems (e.g. hypothesis/model testing); final written report is based on analysis of computer-generated data, the results are discussed in a form of colloquium.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Předmět určen pro studenty Fyzikálního inženýrství VUT.
Teacher's information
http://nymeria.physics.muni.cz/face/praxis/fdoc/mmzm/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019.
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