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Course detail

Analysis of Engineering Experiment

Course unit code: FSI-TAI
Academic year: 2016/2017
Type of course unit: compulsory
Level of course unit: Master's (2nd cycle)
Year of study: 1, 2
Semester: summer
Number of ECTS credits:
Learning outcomes of the course unit:
Students acquire needed knowledge from the mathematical statistics, which will enable them to evaluate and develop stochastic models of technical phenomena and processes based on these methods and realize them on PC.
Mode of delivery:
90 % face-to-face, 10 % distance learning
Descriptive statistics, probability, random variable, random vector, random sample, parameters estimation, hypotheses testing, and regression analysis.
Not applicable.
Recommended optional programme components:
Not applicable.
Course contents (annotation):
The course is concerned with the selected parts of mathematical statistics for stochastic modeling of the engineering experiments: analysis of variance (ANOVA), regression models, nonparametric methods, multivariate methods, and probability distributions estimation. Computations are carried out using the software as follows: Statistica, Minitab, and QCExpert.
Recommended or required reading:
Ryan, T. P.: Modern Regression Methods. New York : John Wiley, 2004.
Anděl, J.: Statistické metody. Praha: Matfyzpress, 2003.
Hebák, P. et al: Vícerozměrné statistické metody 1, 2, 3. Praha : Informatorium, 2004.
Montgomery, D. C. - Renger, G.: Applied Statistics and Probability for Engineers. New York: John Wiley & Sons, 2003.
Meloun, M. - Militký, J.: Statistické zpracování experimentálních dat. Praha: Plus, 1994.
Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering. New York: John Wiley & Sons, 1994.
Planned learning activities and teaching methods:
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes:
Course-unit credit requirements: active participation in seminars, mastering the subject matter, and delivery of semester assignment. Examination (written form): a practical part (3 tasks), a theoretical part (3 tasks); ECTS evaluation used.
Language of instruction:
Work placements:
Not applicable.
Course curriculum:
Not applicable.
The course objective is to make students majoring in Mathematical Engineering and Physical Engineering acquainted with important selected methods of mathematical statistics used for a technical problems solution.
Specification of controlled education, way of implementation and compensation for absences:
Attendance at seminars is controlled and the teacher decides on the compensation for absences.

Type of course unit:

Lecture: 26 hours, optionally
Teacher / Lecturer: doc. RNDr. Zdeněk Karpíšek, CSc.
Syllabus: 1.One-way analysis of variance.
2.Two-way analysis of variance.
3.Regression model identification.
4.Nonlinear regression analysis.
5.Regression diagnostic.
6.Nonparametric methods.
7.Correlation analysis.
8.Principle components.
9.Factor analysis.
10.Cluster analysis.
11.Continuous probability distributions estimation.
12.Discrete probability distributions estimation.
13.Stochastic modeling of the engineering problems.
seminars in computer labs: 13 hours, compulsory
Teacher / Lecturer: doc. RNDr. Zdeněk Karpíšek, CSc.
Syllabus: 1.PC professional statistical software.
2.One-way analysis of variance.
3.Two-way analysis of variance.
4.Regression model identification. Semester work assignment.
5.Nonlinear regression analysis.
6.Regression diagnostic.
7.Nonparametric methods.
8.Correlation analysis.
9.Principle components. Factor analysis.
10.Cluster analysis.
11.Probability distributions estimation.
12.Semester works presentation I.
13.Semester works presentation II.

The study programmes with the given course