Lecturer(s)


Bajzík Vladimír, doc. Ing. Ph.D.

Porkertová Jindra, Ing.

Course content

Lectures: 12. 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 noncompliance 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 onedimensional 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. 1112. Introduction to data treatment for data from nominal and ordinal scales 1314. Contingency tables. Exercises: 1. Calculations of basic characteristics  arithmetic mean, variance, coefficient of variation. 2. Random selection conditions  the effect of noncompliance 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

Selfstudy (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 onedimensional 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 onedimensional data treatment from experiments. Student will be able to interpret the reached results.

Prerequisites

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 8085943182.

Meloun M., Militký J. Kompendium statistického zpracování dat. Praha, Academia, 2002. ISBN 8020010084.
