Statistical Methods in Engineering (FSI-PST)

Academic year 2018/2019
Supervisor: Ing. Josef Bednář, Ph.D.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
We want to show the importance of statistics in engineering and we have taken two specific measures to accomplish this goal. First, to explain that statistics is an integral part of engineer's work. Second, we try to present a practical example of each topic as soon as possible.
Learning outcomes and competences:
Data analysis, descriptive statistics, sample, population, testing hypothesis
Prerequisites:
basic mathematics
Course contents:
Technicians sometimes use statistics to describe the results of an experiment. This process is referred to as data analysis or descriptive statistics. Technicians also use statistics another way. If the entire population of interest is not accessible to them, they often observe only a portion of the population (a sample) and use statistics to answer questions about the whole population. This process called inferential statistics is the main focus of the course.
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 omly
Controlled participation in lessons:
Make ones own work
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 1 hrs. optionally                  
Course curriculum:
    Lecture 1. Collection of data.
2. Variance.
3. Pareto analysis.
4. Probability density and probability distribution.
5. Normal distribution.
6. Distribution of averages
7. Estimation of parameters.
8. Hypothesis testing.
9. Analysis of variances. One way testing,
10. Two way testing.
11. Tukey's method. Scheffe method.
12. Linear model.
13. Coefficient of correlation. Partial coefficient of correlation.
14. Statistics modelling. Monte Carlo method.
    Computer-assisted exercise 1. Collection of data.
2. Variance.
3. Pareto analysis.
4. Probability density and probability distribution.
5. Normal distribution.
6. Distribution of averages
7. Estimation of parameters.
8. Hypothesis testing.
9. Analysis of variances. One way testing,
10. Two way testing.
11. Tukey's method. Scheffe method.
12. Linear model.
13. Coefficient of correlation. Partial coefficient of correlation.
14. Statistics modelling. Monte Carlo method.
Literature - fundamental:
1. Egermayer,F.-Boháč,M.:Statistika pro techniky, SNTL,1984
2. Bakytová,H.: Základy štatistiky, ALFA, 1975
3. Montgomery, D.C.: Introduction to Statistical Quality Control, John Wiley&Sons, Inc., 2001
Literature - recommended:
1. J. Anděl: Statistické metody, , 0
2. A. Linczenyi: Inžinierska štatistika, , 0
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-STM Manufacturing Technology and Management in Industry P linked to branch B-STG Cr,Ex 4 Compulsory 2 1 S
M2I-P full-time study M-STM Manufacturing Technology and Management in Industry -- Cr,Ex 5 Compulsory 2 1 S
M2I-P full-time study M-STG Manufacturing Technology P linked to branch B-STG Cr,Ex 4 Elective (voluntary) 2 1 S
B3A-P full-time study B-MAI Mathematical Engineering -- Cr,Ex 4 Elective (voluntary) 1 3 S
M2I-P full-time study M-SLE Foundry Technology P linked to branch B-STG Cr,Ex 3 Compulsory-optional 2 1 S
M2I-P full-time study M-SLE Foundry Technology -- Cr,Ex 3 Compulsory-optional 2 1 S
B3S-P full-time study B-STI Fundamentals of Mechanical Engineering -- Cr,Ex 4 Elective (voluntary) 1 3 S