Course detail
Planning and Evaluation of Experiments
FSI-TPXAcad. year: 2017/2018
The course deals with the following topics: Basic terms and tasks. Construction of a statistical model, bias correction. Orthogonal functions, tests of residuals, addition of parameters and generalisation error. Correlation, rank-test and maximal correlation, principal components. Upper limits, significance of structures at noise level. Instructive solutions of tasks with illustrative experiments.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Humlíček J.: Statistické zpracování výsledků měření, skriptum MU Brno (CS)
Pánek P.: Úvod do fyzikálního měření, skriptum MU Brno (CS)
R.J.Barlow: Statistics (Guide to the Use of Statistical Methods in Physical Sciences), Wiley (Manchester physics series) 1989 (EN)
Recommended reading
J. HUMLÍČEK: Statistické metody zpracování výsledků měření. MU, Brno 2001. (CS)
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
Models
- construction of parametric and nonparametric (interpolation, smoothing) models
- number of degrees of freedom (polynomial fits), bias-variance trade-off
Fitting - gaussian curve analysis, parameter estimation from histogram, correlation suppression
Combination of measurements, stratification
Correlations
- distribution of correlation parameter, approximation and tests
- parameters around regression minima
- orthogonal regression
Hypothesis testing (Neyman-Persion test)
Confidence intervals
- frequentist interpretation, belt construction
- example of binomial distr. (Clopper-Pearson limit)
- conversion of measurements to confidence intervals
- upper limits (Bayesian construction, Poissonian statistics)
Fine structure - peak identification, variance estimates, statistical significance of multiple peaks
Frequency analysis
- Fourier reconstruction
- Lomb-Scargle algorithm (uneven sampling)
- construction of a periodogram
Examples from experiments
- refraction (Abbe refractometer)
- spectroscopy (transmission measurements)
Extras - constrained optimization, correction of bias, robust methods
Exercise
Teacher / Lecturer
Syllabus