| Course Unit Code | 617-3015/01 |
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| Number of ECTS Credits Allocated | 6 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | Second Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| PRA37 | prof. Ing. Petr Praus, Ph.D. |
| VON37 | Ing. Jiřina Vontorová, Ph.D. |
| Summary |
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| The aim of the lectures is to acquaint the students with the issue of the probability, statistics and data processing. Students will study how to use the validation procedures and how to evaluate correctly the measured data. The focus of the practical exercises is on the training of the computer software for chemometric calculations and other applications. Another aim of the practical courses is to help the students with the processing of their own data measured within diploma thesis and the treatment of the data obtained during other research activities. |
| Learning Outcomes of the Course Unit |
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Knowledge of the basic chemometrics’ terms, validation parameters and methods of statistical treatment of experimental data. Practical utilization of the available software for chemometrics calculations and for processing of measured data.
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| Course Contents |
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1. Definition of the chemometry. Theory of the probability and its definition. Conditional probability.
2. Random variable. Definition of random variable. Discrete and continuous random variable. Histogram, histogram design. Polygon.
3. Theory of errors. Classification of the errors. Propagation of the errors. Precision and accuracy.
4. Analysis of one-dimensional data. Types of probability distributions.
5. Moment characteristics of one-dimensional data. Shape, position and variability. Quantile and robust characteristics.
6. Verification of the independence, normality and homogeneity of the data set.
7. Analysis of small data sets. Horn's method.
8. Statistical tests. Testing of the results; testing of the accuracy of the average value; conformity testing of two average values; testing of the conformity of two standard deviation values.
9. Analysis of variance, ANOVA. One- way ANOVA. Two- way ANOVA.
10. Multidimensional analysis: factor analysis, principle component analysis, discriminatory analysis, cluster analysis.
11. Validation. Definition of the validation, validation process, types of validation, validation parameters (accuracy of the method, repeatability, output error, deviation, outliers identification, confidence interval, determination of the uncertainties, recovery, robustness of analytical procedure.
12. Calibration. Calibration procedure; linearity - estimation of the correlation coefficient; linear equation; linear regression parameters - standard deviation; parameter regression equation test; limits of the detection;
13. Nonlinear calibration. Standard addition method.
14. Linear regression. Effective data (outliers observations, extremes).
Exercice
1. Introductory exercise, probability calculation.
2. Histogram, histogram design. Polygon. (Using MS-Excel and QC-Expert software.)
3. Analysis of one-dimensional data. Types of probability distributions. Moment characteristics of one-dimensional data. Shape, position and variability. Quantile and robust characteristics. (Using MS-Excel and QC-Expert software.)
4. Verification of the independence, normality and homogeneity of the data set. (Using MS-Excel and QC-Expert software.)
5. Analysis of small data sets. Horn's method. (By computing and using QC-Expert software.)
6. Assignment and processing of 1. credit work.
7. Statistical tests. Testing of the results; testing of the accuracy of the average value; conformity testing of two average values; testing of the conformity of two standard deviation values.
8. Analysis of variance, ANOVA. One- way ANOVA. Two- way ANOVA.
9. Multidimensional analysis: factor analysis, principle component method, discriminatory analysis, cluster analysis.
10. Validation. Validation parameters (accuracy of the method, repeatability, output error, deviation, outliers identification, confidence interval, determination of the uncertainties, recovery, robustness of analytical procedure.)
11. Calibration. Calibration procedure; linearity - estimation of the correlation coefficient; linear equation; linear regression parameters - standard deviation; parameter regression equation test; limits of the detection.
12. Linear regression. Effective data (outliers observations, extremes).
13. Assignment and processing of 2. credit work. (Using MS-Excel and QC-Expert software.)
14. Control of credit works, credit.
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| Recommended or Required Reading |
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| Required Reading: |
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| KRAMER, R. Chemometric techniques for quantitative analysis. Boca Raton: CRC Press, 1998. ISBN 0-8247-0198-4. |
MELOUN, M., MILITKÝ, J. Kompendium statistického zpracování dat. Praha: Academia Praha, 2006. ISBN 80-200-1396-2.
MELOUN, M., MILITKÝ, J. Sbírka úloh - Statistické zpracování experimentálních dat, Univerzita Pardubice, 1996. ISBN 80-7194-075-5. |
| Recommended Reading: |
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| GRETON, R.G. Applied chemometrics for scientists. Hoboken: Wiley, 2007. ISBN 978-0-470-01686-2. |
ECKSCHLAGER, K. Chemometrie [Skripta]. Praha: UK, 1991. ISBN 80-7066-487-8.
ČSN EN ISO/IEC 17025:2005 Posuzování shody – Všeobecné požadavky na způsobilost zkušebních a kalibračních laboratoří. Praha: Český normalizační institut, 2005.
ČSN ISO 10576-1:2004 Statistické metody – Směrnice pro hodnocení shody se specifikovanými požadavky – Část 1: Obecné principy. Praha: Český normalizační institut, 2004. |
| Planned learning activities and teaching methods |
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| Lectures, Experimental work in labs, Other activities |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 45 | 21 |
| Examination | Examination | 55 | 6 |