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Lecturer(s)
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Course content
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unspecified
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Learning activities and teaching methods
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Self-study (text study, reading, problematic tasks, practical tasks, experiments, research, written assignments), Independent creative and artistic activities, Individual consultation, Seminár
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Learning outcomes
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The content of the course is practical and theoretical basics of algorithmic collection of technological, laboratory and test data collection and management and their evaluation in terms of production quality, process stability in larger production units and corporate companies with multiple production plants. It focuses on tools for data collection, storage and management, mathematical probabilistic models and statistical methods and their use in quality and stability measurement, numerical and information aspects and computer arithmetic. The course focuses on theoretical and applied problems of data filtering and validation, measurement of process fitness and performance, process stability, solving special cases of singularity, indeterminacy and efficiency. The aim of the course is to provide mathematical and technical tools for scaled performance evaluation at the operation/product level up to the structured corporation level and methods for aggregation, global comparison and assessment of trends in efficiency and quality of production for optimal managerial decision-making.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
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Recommended literature
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R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical, 2012.
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KUPKA, K. Darwin. Definice a popis jazyka. Pardubice: TriloByte, 2011.
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MELOUN M., MILITKÝ, J. Interaktivní statistická analýza dat. Praha: Karolinum, 2012. ISBN 978-8-024-62173-9.
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RYAN, T.P. Statistical Methods for Quality Improvement. New York: Wiley-Interscience, 2000. ISBN 9780471 197751.
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