Analysis of Engineering Experiment
FSI-TAIAcad. year: 2018/2019
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.
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.
Descriptive statistics, probability, random variable, random vector, random sample, parameters estimation, hypotheses testing, and regression analysis.
Recommended optional programme components
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
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.
Classification of course in study plans
- Programme M2A-P Master's
branch M-FIN , 1. year of study, summer semester, 3 credits, compulsory
branch M-PMO , 1. year of study, summer semester, 4 credits, compulsory-optional
branch M-MAI , 2. year of study, summer semester, 4 credits, compulsory
- Programme M2I-P Master's
branch M-KSI , 2. year of study, summer semester, 4 credits, optional (voluntary)
Type of course unit
26 hours, optionally
Teacher / Lecturer
1.One-way analysis of variance.
2.Two-way analysis of variance.
3.Regression model identification.
4.Nonlinear regression 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
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.
9.Principle components. Factor analysis.
11.Probability distributions estimation.
12.Semester works presentation I.
13.Semester works presentation II.
eLearning: currently opened course