Analysis of Engineering Experiment (FSI-TAI)

Academic year 2018/2019
Supervisor: doc. RNDr. Zdeněk Karpíšek, CSc.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
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.
Learning outcomes and competences:
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.
Prerequisites:
Descriptive statistics, probability, random variable, random vector, random sample, parameters estimation, hypotheses testing, and regression analysis.
Course contents:
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.
Teaching methods and criteria:
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.
Controlled participation in lessons:
Attendance at seminars is controlled and the teacher decides on the compensation for absences.
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 1 hrs. compulsory                  
Course curriculum:
    Lecture 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.
    Computer-assisted exercise 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.
Literature - fundamental:
1. Ryan, T. P.: Modern Regression Methods. New York : John Wiley, 2004.
2. Montgomery, D. C., Renger, G.: Applied Statistics and Probability for Engineers. New York: John Wiley & Sons, 2010.
3. Anděl, J.: Základy matematické statistiky. Praha: Matfyzpress, 2011.
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
M2I-P full-time study M-KSI Mechanical Engineering Design -- Cr,Ex 4 Elective (voluntary) 2 2 S
M2A-P full-time study M-FIN Physical Engineering and Nanotechnology -- Cr,Ex 3 Compulsory 2 1 S
M2A-P full-time study M-MAI Mathematical Engineering -- Cr,Ex 4 Compulsory 2 2 S
M2A-P full-time study M-PMO Precise Mechanics and Optics -- Cr,Ex 4 Compulsory-optional 2 1 S