SubjectsSubjects(version: 947)
Course, academic year 2023/2024
Data Processing in Statistics - B323006
Title: Statistické zpracování dat
Guaranteed by: Department of Food Analysis and Nutrition (323)
Faculty: Faculty of Food and Biochemical Technology
Actual: from 2022
Semester: winter
Points: winter s.:3
E-Credits: winter s.:3
Examination process: winter s.:
Hours per week, examination: winter s.:1/2, MC [HT]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
For type: Bachelor's
Additional information:
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Drábová Lucie Ing. Ph.D.
Interchangeability : N323008
This subject contains the following additional online materials
Annotation -
Last update: Drábová Lucie Ing. Ph.D. (16.11.2021)
During the lectures and exercises, students will get acquainted with the basic statistical concepts and methods used to process data sets of biological and food science disciplines. They will be able to practice statistical methods on real examples so that they will then be able to use them in practice. For this reason, the free available mathematico-statistical software Rstudio and MetaboAnalyst are used for teaching. Both of these free available software are suitable both for basic statistical tasks and for subsequent more complex tasks such as multidimensional statistical methods. Basic statistical methods are also practiced in MS Excel. Emphasis is placed on the practical use of statistical methods and the correct interpretation of the results obtained.
Aim of the course -
Last update: Drábová Lucie Ing. Ph.D. (16.11.2021)

Student will be able to:

  • statistically process the obtained (measured) data files using MS Excel, freely available software Rstudio or MetaboAnalyst and correctly interpret the obtained results.
Literature -
Last update: Drábová Lucie Ing. Ph.D. (16.11.2021)

R: Eckschlager K., Horsák I., Kodejš Z.: Vyhodnocování analytických výsledků a metod, SNTL Praha, 1980, ISBN 04-610-80

Electronic resources:

R: Z: M. Meloun, J. Militký : Statistické zpracování experimentálních dat - v chemometrii, biometrii, ekonometrii a v dalších oborech přírodních, technických a společenských věd,

R: Elektronická nápověda k programu MS Excel podle aktuální verze



Learning resources -
Last update: Fialová Jana (18.12.2017)

Requirements to the exam -
Last update: Vlčková Martina Ing. (30.01.2018)

Within the extent of the syllabus.

Syllabus -
Last update: Pulkrabová Jana prof. Ing. Ph.D. (30.01.2018)

1. History introduction, histogram, random value.

2. Frequency - absolute, relative, cumulative and relative cumulative, probability, random selection, variartion, permutation, combination.

3. Frequency and distributin function.

4. Absolute and relative error, statistical estimations, mean value, variance, curtoisis, skewnes.

5. Statistical tests - parametric.

6. Statistical tests - nonparametric.

7. Regression - linear.

8. Regression - nonlinear, polynomic, linearization.

9. Correlation, coefficient and matrix.

10. Analysis of variance.

11. Introduction to the multivariate methods.

12. Basic principles of the neural networks.

13. Using fuzzy sets.

14. New trends in data processing.

Registration requirements -
Last update: Pulkrabová Jana prof. Ing. Ph.D. (30.01.2018)

Mathematics I

Course completion requirements -
Last update: Fialová Jana (18.12.2017)

Individual statistical project

Teaching methods
Activity Credits Hours
Účast na přednáškách 0.5 14
Práce na individuálním projektu 1 28
Příprava na zkoušku a její absolvování 0.5 14
Účast na seminářích 1 28
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