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Last update: Kubová Petra Ing. (04.01.2018)
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Last update: Kubová Petra Ing. (04.01.2018)
Students will be able to: process data from laboratory experiments and industrial measurements, comprehensively and reliably interpret the results of statistical data processing, propose optimal design of experiments, operate representative software for statistical data processing. |
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Last update: Kubová Petra Ing. (04.01.2018)
A: Joglekar A. M.: Industrial statistics. Wiley, Hoboken 2010. 9780470497166 |
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Last update: Paterová Iva Ing. Ph.D. (08.09.2023)
https://e-learning.vscht.cz/course/view.php?id=1164 |
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Last update: Paterová Iva Ing. Ph.D. (08.09.2023)
1. Podmínkou udělení zápočtu je zvládnutí základních počítačových programů pro zpracování a vyhodnocování dat, prověřené na základě vyřešení 4 domácích úkolů zadaných během semestru. 2. Zkouška je založena na samostatném zpracování vybraného souboru praktických příkladů s možností využití libovolných pomůcek. |
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Last update: Kubová Petra Ing. (04.01.2018)
1. Principles of data analysis, properties of the measured data, experiments versus observations. 2. Statistical analysis of the data, sample problems, application software. 3. Direct acquisition of information from measured data, analysis of the sample characteristics. 4. Time series analysis, data sorting, design of experiments. 5. Mathematical models, mechanistic, empirical and semi-empirical models. 6. Methods of optimal estimation of model parameters, software for regression analysis. 7. Models with differential equations, derivatives of dependent variables, integration of differential equations. 8. Evaluating the reliability of regression parameters, confidence intervals, correlation of parameters. 9. Evaluating the reliability of simulated data, analysis of variance and residual variation. 10. Treatment of data for regression analysis, elimination of remote measurements, transformation of variables. 11. Treatment of regression models, model transformation, elimination of strong correlation of parameters. 12. Design of experiments, the optimum number of responses and the range of experimental conditions. 13. Sequential design of experiments, model discrimination and refinement. 14. Factorial and empirical design of experiments, full and fractional factorial design. |
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Last update: Kubová Petra Ing. (04.01.2018)
Mathematics I, Applied Statistics |