Course detail
Analysis of Engineering Experiment
FSI-TAI-AAcad. year: 2024/2025
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.
Language of instruction
English
Number of ECTS credits
5
Mode of study
Not applicable.
Guarantor
Department
Entry knowledge
Descriptive statistics, probability, random variable, random vector, random sample, parameters estimation, hypotheses testing, and regression analysis.
Rules for evaluation and completion of the course
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.
Attendance at seminars is controlled and the teacher decides on the compensation for absences.
Attendance at seminars is controlled and the teacher decides on the compensation for absences.
Aims
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.
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.
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.
Study aids
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering. New York: John Wiley & Sons, 1994.
Montgomery, D. C. - Renger, G.: Applied Statistics and Probability for Engineers. New York: John Wiley & Sons, 2003.
P. Hebák, J. Hustopecký: Vícerozměrné statistické metody, SNTL, Praha 1990
Ryan, T. P.: Modern Regression Methods. New York : John Wiley, 2004.
Montgomery, D. C. - Renger, G.: Applied Statistics and Probability for Engineers. New York: John Wiley & Sons, 2003.
P. Hebák, J. Hustopecký: Vícerozměrné statistické metody, SNTL, Praha 1990
Ryan, T. P.: Modern Regression Methods. New York : John Wiley, 2004.
Recommended reading
Anděl, J.: Statistické metody. Praha: Matfyzpress, 2003.
Hebák, P. et al: Vícerozměrné statistické metody 1, 2, 3. Praha : Informatorium, 2004.
Meloun, M. - Militký, J.: Statistické zpracování experimentálních dat. Praha: Plus, 1994.
Hebák, P. et al: Vícerozměrné statistické metody 1, 2, 3. Praha : Informatorium, 2004.
Meloun, M. - Militký, J.: Statistické zpracování experimentálních dat. Praha: Plus, 1994.
Classification of course in study plans
- Programme N-MAI-A Master's 2 year of study, summer semester, compulsory
Type of course unit
Lecture
26 hod., optionally
Teacher / Lecturer
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.
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.
Computer-assisted exercise
13 hod., compulsory
Teacher / Lecturer
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.
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.