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Lecturer(s)
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Milde David, doc. Ing. Ph.D.
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Course content
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It is focused on multivariate data analysis; practical examples from chemistry are included. In seminars, examples are solved with the help of statistical software. Content: multivariate data and its parameters; data transformation; exploratory data analysis, outliers; principal component analysis - interpretation of principal components; factor analysis - factor rotation and interpretation; cluster analysis, dendrograms, hierarchical and nonhierarchical clustering methods; nonlinear regression - construction of the regression model, hypothesis testing, confidence interval, reliability of nonlinear regression.
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Learning activities and teaching methods
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Dialogic Lecture (Discussion, Dialog, Brainstorming)
- Attendace
- 26 hours per semester
- Homework for Teaching
- 13 hours per semester
- Preparation for the Course Credit
- 51 hours per semester
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Learning outcomes
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The aim is to acquaint with basics of multidimensional statistics for chemistry.
Associate information, interpret the experimental data, solve the problems of chemometrics
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Prerequisites
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The knowledge of basics of analytical chemistry and instrumental methods of analytical chemistry is recommended.
ACH/CHEX1
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Assessment methods and criteria
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Written exam
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Recommended literature
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Ellison S.L.R., Barwick V.J., Farrant TJ.D.: Practical statistics for the analytical scientist. RSC, London 2009.
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Eurachem-ČR, Ústí nad Labem 2018. (ISBN 978-80-86322-11-7).
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Hebák P., Hustopecký J. a kol.: Vícerozměrné statistické metody 3. Informatorium, Praha 2005..
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Meloun M., Militký J.: Kompendium statistického zpracování dat. Academia, Praha 2002..
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Meloun M., Militký J.: Počítačová analýza vícerozměrných dat v příkladech. Academia Praha 2005.
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Milde D. (ed.): Kvalimetrie 23: Měření v chemii. Stručný přehled metrologie v chemii..
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