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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 univariate data analysis; practical examples from chemistry are included. In seminars, examples are solved with the help of statistical software. Content: statistical measures of place, spread and shape; statistics of repeated measurements - confidence intervals; exploratory data analysis and data assumptions; data transformation; hypothesis testing (comparison of mean with a known value, comparison of the means of two samples, paired test, outliers); analysis of variance; introduction into regression analysis; linear regression (polynomic regression and multidimensional regression is also included); least squares method; regression diagnostics, correlation; calibration.
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming)
- Attendace
- 52 hours per semester
- Homework for Teaching
- 13 hours per semester
- Preparation for the Course Credit
- 26 hours per semester
- Preparation for the Exam
- 29 hours per semester
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Learning outcomes
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The aim is to acquaint with basics of statistics for chemistry.
Define the main chemometric conceptions, recognize the statistical methods
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Prerequisites
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The knowledge of basics of analytical chemistry and instrumental methods of analytical chemistry is recommended.
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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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M. Meloun, J. Militký: Interaktivní statistická analýza dat, Karolinum Praha 2012..
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Meloun M., Militký J.: Kompendium statistického zpracování dat. Academia, Praha 2006..
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Meloun M., Militký J.: Statistické zpracování experimentálních dat. Plus Praha, 1994..
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