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On-line statistical process control (Shewart CUSUM and EWMA control charts). Off-line statistical process control
(detection
of changes of parameters in the location model, in the model of linear regression and in the autoregression model,
distribution
of extremes).
Sampling plans for inspection by attributes (case of isolated lots, continuous production,
intermittent control). Sampling plans for inspection by measurement for univariate and multivariate data (normally
and not
normally distributed data).
Requirements:basics of probability and statistics.
Last update: T_KPMS (16.05.2013)
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The students will acquaint with the on-line and off-line stochastic approach to the quality control and with the methods of statistical sampling. Aside mathematical methods there will be discussed but also also their implementation in European norms. Last update: T_KPMS (16.05.2013)
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Oral exam. Last update: Zichová Jitka, RNDr., Dr. (14.06.2019)
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Státní normy z oblasti řízení jakosti, zejména
ČSN 01 02 65, Statistická regulace
ČSN ISO 78 70, Regulační diagramy
ČSN 010254, Statistická přejímka srovnáváním
Sarkadi-Vincze,J.: Mathematical Methods of Quality Control
Beljajev,Ju., Solovjev, V. M.: Vyboročnyj metod kontrolja kačestva Last update: T_KPMS (16.05.2013)
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Lecture+exercises. Last update: T_KPMS (16.05.2013)
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Examination is oral within a framework of discussed matter given by the syllabus a scope presented during the lecture. TODO It is necessary to know all fundamental definitions, theorems and assertions (including the assumptions), understand ther inter relations and be capable in outline explain their justification (proofs). Student should be able to analyze real problems. Last update: Antoch Jaromír, prof. RNDr., CSc. (28.02.2019)
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1. On-line statistical process control (Shewart control charts, CUSUM control charts, EWMA control charts).
2. Off-line statistical process control (detection of changes of parameters in the location model, detection of changes of parameters in the model of linear regression, detection of changes of parameters in the autoregression model, distribution of extremes).
3. Sampling plans for inspection by attributes (case of isolated lots, continuous production, intermittent control).
4. Sampling plans for inspection by measurement for univariate and multivarialte data (normally distributed data, not normally distributed data). Last update: T_KPMS (16.05.2013)
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Random variables and vectors and their characterizations; convergence in distribution, in probability and almost surely; central limit theorem; conditional density and conditional expectation. Last update: Antoch Jaromír, prof. RNDr., CSc. (04.06.2018)
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