C5981 Analýza dat a chemometrie v ochraně kulturního dědictví

Faculty of Science
Autumn 2008
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
Teacher(s)
Mgr. Ing. Lubomír Prokeš, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Jiří Příhoda, CSc.
Department of Chemistry – Chemistry Section – Faculty of Science
Timetable
Tue 15:00–16:50 C12/311
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The main goal ot this course is to master mathematical methods of data analysis.
Syllabus
  • 1. Principal concepts, probability, Bayes theorem, uncertainty and maze (fuzzy sets).
  • 2. Digital and graphic data reprezentation (descriptive statistics), distribution of data and distribution function, transformation and normalization. Simulation methods (Monte Carlo, Bootstrap and Jacknife).
  • 3. Measuring errors, exactness and accuracy, reproducibility and repeatability of results. Exactness of calculated values, rounding. Analytical signal and signal noice. Limits of detection and determination. Tolerance and prediction intervals.
  • 4. One-dimensional analysis of data (punctual and interval estimations, hypothesis testing, test power, non-parametric tests).
  • 5. Stochastic selection, randomization, ANOVA, experimental design.
  • 6. Analysis of a plot: correlation, regression analysis, weight and orthogonal regression. Analysis of residues, Bland-Altman graph. Calibration, validation of new methods. Non-linear regression, linearization. Multiple linear regression.
  • 7. Extrapolation and interpolation, numeric smoothing and approximation: polynoms a spleens, method of moving average, Savitzky-Golay method, kernels, discreet Fourier transform. Numeric derivation a integration. Convolution a deconvolution. 8. Control charts, analysis od time series.
  • 9. Analysis of categorial data, contingent tables.
  • 10. Digital and graphic reprezentation of more-dimensional data. Classificatory and regression trees. Growth curves and survival analysis.
  • 11. Multidimensional contingent tables, correspondence analysis.
  • 12. Advanced methods of data processing and pattern recognition methods. Metaanalysis of data and data mining. Artificial intelligence methods (artifitial neuron nets, fuzzy methods, genetic optimalization).
Literature
  • Meloun M., Militký J.: Kompendium statistického zpracování dat. Academia, Praha, 2001.
  • Meloun M., Militký J., Hill M.: Počítačová analýza vícerozměrných dat v příkladech. Academia, Praha 2005.
  • Montgomery D. C., Runger G. C.: Applied Statistics and Probability for Engineers. 3rd Ed., Wiley, New York,
  • Hendl J.: Přehled statistických metod zpracování dat. Portál, Praha, 2004.
  • Berthouex P. M., Brown L. C.: Statistics for Environmental Engineers. 2nd Ed., Lewis Publishers, Boca Raton, 2002.
  • Eckschlager K., Horsák I., Kodejš Z.: Vyhodnocování analytických výsledků a metod. SNTL, Praha, 1980.
Assessment methods
Lecture, colloquium
Language of instruction
Czech
Further Comments
Study Materials
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2007.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2008/C5981