Course detail
Analysis of Engineering Experiment
FSI-TAIAcad. year: 2024/2025
The course is concerned with the selected parts of mathematical statistics for stochastic modeling of the engineering experiments: regression models, regression diagnostics, multivariate methods, probability distributions estimation, interval statistical analysis, and fuzzy statistics. Computations are carried out using the software as follows: Statistica, Minitab, and Excel..
Language of instruction
Number of ECTS credits
Mode of study
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 (5 tasks), a theoretical part (5 tasks); ECTS evaluation used.
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 and interval models of technical phenomena and processes based on these methods and realize them on PC.
Study aids
Prerequisites and corequisites
Basic literature
Hebák, P., Hustopecký, J., Jarošová, E., Pecáková, I.: Vícerozměrné statistické metody 1, 2, 3, Praha: INFORMATORIUM, 2004. (CS)
Montgomery, D. C., Renger, G.: Applied Statistics and Probability for Engineers. New York: John Wiley & Sons, 2010. (EN)
Ryan, T. P.: Modern Regression Methods. New York : John Wiley, 2004. (EN)
Recommended reading
Klir, G. J., Yuan, B.: Fuzzy Sets and Fuzzy Logic. New Jersey: Prentice Hall 1995. (EN)
Moor, R. E., Kearfott, R. B., Clood, M. J.: Introduction to Interval Analysis. Philadelphia: SIAM 2009. (EN)
Elearning
Classification of course in study plans
- Programme N-FIN-P Master's 1 year of study, summer semester, compulsory
- Programme N-MAI-P Master's 2 year of study, summer semester, compulsory
- Programme N-PMO-P Master's 1 year of study, summer semester, compulsory-optional
- Programme C-AKR-P Lifelong learning
specialization CLS , 1 year of study, summer semester, elective
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Stochastic modeling of the engineering problems.
- Regression model identification.
- Linear regression models and diagnostic.
- Nonlinear regression analysis.
- Correlation analysis.
- Principle components and factor analysis.
- Cluster analysis.
- Bootstrap estimates.
- Continuous probability distributions estimation.
- Discrete probability distributions estimation.
- Interval analysis.
- Interval statistical models.
- Fuzzy statistics.
Computer-assisted exercise
Teacher / Lecturer
Syllabus
- PC statistical software.
- Regression model identification. Semester work assignment.
- Linear regression models and diagnostic.
- Nonlinear regression models.
- Correlation analysis.
- Principle components and factor analysis.
- Cluster analysis.
- Bootstrap estimates.
- Continuous probability distributions estimation.
- Discrete probability distributions estimation.
- Interval analysis.
- Interval statistical models.
- Fuzzy statistics.
Elearning