Course: Statistical Methods for Data Processing

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Course title Statistical Methods for Data Processing
Course code KAM/NNSZD
Organizational form of instruction Lecture + Tutorial
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course unspecified
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Javůrek Milan, doc. Ing. CSc.
Course content
Topics of lectures after weeks of semester 1. Introduction to metrology, types of measurement errors and their detection, classification of measuring instruments. 2. Repeated measurements of static data, evaluation methods using mathematical statistics. 3. Exploratory data analysis, robust and classical statistics, data transformation. 4. Classical numerical tests of data normality and outliers. 5. Statistical hypothesis testing, classical and robust approach. 6. Analysis of variance, different evaluation methods. 7. Dynamic data measurement. Regression method, usage rules. Different types of residues. 8. Linear regression, methods of data criticism, model testing. Different types of models. 9. Problems in using linear regression (heteroskedasticity, multicollinearity), their detection and solution. 10. Different types of linear regression? polynomial, sequential, multiple, model search. 11. Calibration method, principle and different methods of calibration table determination. 12. Nonlinear regression, principle, description of basic methods, problems in calculations. 13. Principles of using nonlinear regression programs, calculation procedures, identification of errors.

Learning activities and teaching methods
unspecified, Monologic (reading, lecture, briefing)
  • Home preparation for classes - 150 hours per semester
Learning outcomes
The aim of the course is to teach students to evaluate numerical experimental data by suitable mathematical methods and to interpret the results correctly.
Student after passing the subject - demonstrates the knowledge of the use of statistical methods and available software, knows the prerequisites of using individual evaluation methods. - It is able to independently select and use appropriate evaluation methods by data type.
Prerequisites
Basic overview of statistical methods. Good knowledge of Excel.

Assessment methods and criteria
Oral examination

The condition for obtaining the credit is the elaboration of all 13 individual tasks assigned at seminars. If three possible attempts are exhausted, a replacement task is provided. Two examples for individual evaluation and one overview question are given during the examination.
Recommended literature
  • JAVŮREK, M., TAUFER, I. Vyhodnocování experimentálních dat. 2. vydání. Pardubice, 2018. ISBN 978-80-270-3611-0.
  • MELOUN, M., MILITKÝ, J. Kompendium statistického zpracování dat.. Praha, 2006. ISBN 80-200-1396-2.


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