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Course detail

Statistical Methods in Engineering

Course unit code: FSI-PST-K
Academic year: 2016/2017
Type of course unit: compulsory
Level of course unit: Master's (2nd cycle)
Year of study: 1
Semester: summer
Number of ECTS credits:
Learning outcomes of the course unit:
Data analysis, descriptive statistics, sample, population, testing hypothesis
Mode of delivery:
20 % face-to-face, 80 % distance learning
Prerequisites:
basic mathematics
Co-requisites:
Not applicable.
Recommended optional programme components:
Not applicable.
Course contents (annotation):
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.
Recommended or required reading:
J. Anděl: Statistické metody, , 0
Egermayer,F.-Boháč,M.:Statistika pro techniky, SNTL,1984
A. Linczenyi: Inžinierska štatistika, , 0
Bakytová,H.: Základy štatistiky, ALFA, 1975
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 omly
Language of instruction:
Czech
Work placements:
Not applicable.
Course curriculum:
Not applicable.
Aims:
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.
Specification of controlled education, way of implementation and compensation for absences:
Make ones own work

Type of course unit:

Tuition: 13 hours, optionally
Teacher / Lecturer: Ing. Josef Bednář, Ph.D.
Syllabus: 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.
Controlled Self-study: 26 hours, optionally
Teacher / Lecturer: Ing. Josef Bednář, Ph.D.
Syllabus: 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.

The study programmes with the given course