Course: Data Treatment

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Course title Data Treatment
Course code KMI/ZPD
Organizational form of instruction Lecture + Lesson
Level of course Bachelor
Year of study not specified
Semester Winter
Number of ECTS credits 4
Language of instruction Czech, English
Status of course Compulsory
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
  • Bajzík Vladimír, doc. Ing. Ph.D.
  • Porkertová Jindra, Ing.
Course content
Lectures: 1-2. Introduction to data processing, interpretation of basic concepts, mean, variance, point and interval estimates, other parameters associated with data distribution. 3. Basic set and random selection, definition of the basic set, the influence of non-compliance with the conditions of random selection on the result, assumptions about data, possibilities of verification of assumptions about data. 4. Measurement errors - sources of errors in the experiment, relative and absolute measurement error, additive measurement model. 5. Exploratory analysis of one-dimensional data - order statistics, order probability, letter quantiles, use of letter quantiles in exploratory data analysis. 6. Verification of normality, hypothesis tests. 7. Verification of normality - graphically. 8. Verification of symmetry - creation of histogram and box plot. 9. Correlation, linear regression - definition of the term, least squares method, confidence intervals of regression parameters. 10. Use of statistical software in solving survey data analysis. 11-12. Introduction to data treatment for data from nominal and ordinal scales 13-14. Contingency tables. Exercises: 1. Calculations of basic characteristics - arithmetic mean, variance, coefficient of variation. 2. Random selection conditions - the effect of non-compliance on the results. 3. Size of random selection, Horn's procedure. 4. Measurement errors. 5. Order statistics, letter quantiles. 6. Verification of normality - hypothesis tests. 7. Control test I. 8. Verification of normality graphically. 9. Least squares method I. 10. Least squares method II. 11. Nominal scale. 12. Ordinal scale. 13. Control test II. 14. Credit.

Learning activities and teaching methods
Self-study (text study, reading, problematic tasks, practical tasks, experiments, research, written assignments), Lecture, Practicum
  • Class attendance - 56 hours per semester
  • Class attendance - 16 hours per semester
  • Home preparation for classes - 60 hours per semester
  • Preparation for credit - 40 hours per semester
  • Preparation for formative assessments - 20 hours per semester
Learning outcomes
Students will be familiarized with procedures fro evaluation of data from repeated measurements. They will obtain detaied description of basic statistical characteristics of one-dimensional data dealing with location, variability, skewness and kurtoisis. They will be educated about the possibilities of data mining and searching of typical features of data by using of exploratory data analysis. They will learn to visualize the peculiarities of data and to verify the basic assumptions including idependence, constant variance,homogeneity and normality of data for cardinal scales. They will obtain the basics of data processing for the nominal and ordinal scales also. The explanation will be accompanied by a practical solutions of examples from textile practice.
Students gain experience in one-dimensional data treatment from experiments. Student will be able to interpret the reached results.
they are not required

Assessment methods and criteria
Written assignment

Credit: Active participation in seminars, passing 2 control tests during the semester and the final comprehensive test.
Recommended literature
  • Cyhelský L a kol. Elementární statistická analýza. Praha, management Press, 1996. ISBN 80-85943-18-2.
  • Meloun M., Militký J. Kompendium statistického zpracování dat. Praha, Academia, 2002. ISBN 80-200-1008-4.

Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester