Course detail

Statistical Methods in Engineering

FSI-PSTAcad. year: 2024/2025

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.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Entry knowledge

basic mathematics

Rules for evaluation and completion of the course

Course-unit credit omly
Make ones own work

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.
Data analysis, descriptive statistics, sample, population, testing hypothesis

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Bakytová,H.: Základy štatistiky, ALFA, 1975
Egermayer,F.-Boháč,M.:Statistika pro techniky, SNTL,1984
Montgomery, D.C.: Introduction to Statistical Quality Control, John Wiley&Sons, Inc., 2001

Recommended reading

A. Linczenyi: Inžinierska štatistika, , 0
J. Anděl: Statistické metody, , 0

Classification of course in study plans

  • Programme B-MAI-P Bachelor's 3 year of study, summer semester, elective
  • Programme B-VTE-P Bachelor's 3 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

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.

Computer-assisted exercise

13 hod., optionally

Teacher / Lecturer

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.