SubjectsSubjects(version: 845)
Course, academic year 2018/2019
   Login via CAS
Introduction to statistical data processing in the surface and plasma science - NEVF164
Title in English: Úvod do statistického zpracování dat ve fyzice povrchů a plazmatu
Guaranteed by: Department of Surface and Plasma Science (32-KFPP)
Faculty: Faculty of Mathematics and Physics
Actual: from 2017
Semester: summer
E-Credits: 3
Hours per week, examination: summer s.:2/0 Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Guarantor: RNDr. Petr Dohnal, Ph.D.
prof. RNDr. Ondřej Santolík, Dr.
Annotation -
Last update: T_KEVF (15.05.2017)
The aim of the course is to give introduction to statistical data processing used in physics in general with emphasis on plasma and surface physics. The covered topics are: examples of basic probability distributions, data processing methods, estimation of parameters of linear and nonlinear models and introduction to random processes. Utilization of presented methods will be shown using examples from surface and plasma physics.
Literature - Czech
Last update: T_KEVF (15.05.2017)

Anděl J.: Matematické statistika, SNTL, Praha 1978.

Barlow R.J. , Statistics. A Guide to the Use of Statistical Methods in the Physical Sciences, John Wiley & Sons, 1993.

Press W.H. et al.: Numerical Recipes , Cambridge University Press, Cambridge, 1992.

Tutubalin V.N.: Teorie pravděpodobnosti, SNTL, Praha 1978.

Requirements to the exam - Czech
Last update: RNDr. Petr Dohnal, Ph.D. (01.03.2018)

Zkouška je ústní a požadavky odpovídají sylabu předmětu v rozsahu, který byl prezentován na přednášce.

Syllabus -
Last update: T_KEVF (15.05.2017)
Statistical data processing of experimental data
classical probability, conditional probability, Bayes theorem, random variable, moments of random variable, probability density function and distribution function, examples of probability distributions (discrete,continuous), random vector, estimates and their bias, correlation and covariance, correlation coefficients. Moment-generating function.

Estimation of parameters of models
maximum likelihood estimation, least square method (general linear model), normal equations and their solution, Singular Value Decomposition, examples, estimation of parameters of nonlinear models, Marquardt method, goodness of fit, confidence intervals estimation, non-parametric models.

Random processes
Stationary and ergodic processes, convolution, Fourier transform, power spectrum, Wiener-Khinchin theorem, data sampling, Nyquist frequency, discrete Fourier transform, spectral analysis

Examples of data
processing procedures.

Charles University | Information system of Charles University |