SubjectsSubjects(version: 896)
Course, academic year 2021/2022
Data Processing in Statistics - B323006
Title: Statistické zpracování dat
Guaranteed by: Department of Food Analysis and Nutrition (323)
Actual: from 2021
Semester: winter
Points: winter s.:3
E-Credits: winter s.:3
Examination process: winter s.:
Hours per week, examination: winter s.:1/2 MC [hours/week]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
Language: Czech
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.
Kosek Vít Ing. Ph.D.
Interchangeability : N323008
This subject contains the following additional online materials
Annotation -
Last update: Fialová Jana (18.12.2017)
Basic statistical analysis with small numerical data sets. Working with the actual versions of MS Excel and Statistica for Windows (StatSoft).
Aim of the course -
Last update: Fialová Jana (18.12.2017)

Student will be able to:

  • Use statistic functions and macros in MS Excel, simple statistic analysis with Statistica (modul Basic).
Literature -
Last update: Fialová Jana (18.12.2017)

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

Electronic resources:

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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