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Course, academic year 2023/2024
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Methodology of Measurement in Chemistry - MC230P44
Title: Metodologie měření
Czech title: Metodologie měření
Guaranteed by: Department of Analytical Chemistry (31-230)
Faculty: Faculty of Science
Actual: from 2021
Semester: winter
E-Credits: 2
Examination process: winter s.:
Hours per week, examination: winter s.:2/0, Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Note: enabled for web enrollment
Guarantor: RNDr. Jan Fischer, Ph.D.
Teacher(s): RNDr. Jan Fischer, Ph.D.
prof. RNDr. Jiří Zima, CSc.
Annotation -
Measurement methodology in analytical chemistry. From theory of signal to experimental design and introduction to multivariate data analysis.
Last update: SUCHAN (14.04.2005)
Literature -

Jiří G.K. Ševčík, Metodologie měření v analytické chemii, Karolinum, Praha 1999.
Meloun M., Militký J., Statistické zpracování experimentálních dat, Ars magma, Praha 1998.
Brereton R.G., Chemometrics Data Analysis for the Laboratory and Chemical Plant, Wiley, Chichester 2003.
MillerJ.N., Miller J.C., Statistics and Chemometrics for Analytical Chemistry, Pearson, Harlow, 2000.
Marhold K., Suda J., Statistické zpracování mnohorozměrných dat v taxonomii, Karolinum, Praha 2002.

Last update: Nesměrák Karel, doc. RNDr., Ph.D. (28.10.2019)
Requirements to the exam -

Oral exam.

Last update: Nesměrák Karel, doc. RNDr., Ph.D. (28.10.2019)
Syllabus -

1. Model of a signal, signal parameters, instrumentation parameters evaluation.

2. Signal processing, noise, drift, post-run calculation.

3. Analytical results, precision and accuracy, exploratory univariate data analysis, tests and graphs, statistical identification of a population, population parameters.

4. Methods of statistical analysis, univariate data, parameters, multivariate data, parameters, covariance, regression and correlation.

5. Methods of multivariate data analysis, clustering methods, hierarchical cluster analysis, resemblance coefficients.

6. Methods of multivariate data analysis, principal component analysis, latent variables, plots.

7. Discriminating analysis, canonical, classification analysis.

8. Experimental design and optimization, randomization, blocking.

9. Factorial design and optimization.

10. Factorial design, ANOVA.

11. Multifactorial design, analytical applications.

Last update: Nesměrák Karel, doc. RNDr., Ph.D. (28.10.2019)
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