SubjectsSubjects(version: 941)
Course, academic year 2023/2024
Data Processing in Statistics - N323008
Title: Statistické zpracování dat
Guaranteed by: Department of Food Analysis and Nutrition (323)
Faculty: Faculty of Food and Biochemical Technology
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 [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
For type:  
Additional information:
Guarantor: Pudil František Ing. CSc.
Kocourek Vladimír prof. Ing. CSc.
Is interchangeable with: B323006
Examination dates   Schedule   
Annotation -
Last update: TAJ323 (12.12.2013)
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: TAJ323 (12.12.2013)

Student will be able to:

  • Use statistic functions and macros in MS Excel, simple statistic analysis with Statistica (modul Basic).
Literature -
Last update: TAJ323 (16.08.2013)

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: Pudil František Ing. CSc. (15.08.2013)

Teaching methods -
Last update: Pudil František Ing. CSc. (15.08.2013)

Lectures, seminars and individual work

Requirements to the exam -
Last update: Pudil František Ing. CSc. (15.08.2013)

Lectures, seminars and individual project

Syllabus -
Last update: TAJ323 (16.08.2013)

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.

Entry requirements -
Last update: Pudil František Ing. CSc. (15.08.2013)

Required N413002

Registration requirements -
Last update: Pudil František Ing. CSc. (15.08.2013)

Mathematics I, Computer technology I

Course completion requirements -
Last update: Pudil František Ing. CSc. (15.08.2013)

Individual statistical project

Teaching methods
Activity Credits Hours
Účast na přednáškách 0.5 14
Práce na individuálním projektu 1.5 42
Příprava na zkoušku a její absolvování 1 28
Účast na seminářích 1 28
4 / 3 112 / 84
Coursework assessment
Form Significance
Regular attendance 50
Defense of an individual project 50