Course detail

Statistical Methods in Engineering

FSI-PSTOptional (voluntary)Bachelor's (1st cycle)Acad. year: 2016/2017Summer semester1, 3. year of study4  credits

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.

Learning outcomes of the course unit

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

Mode of delivery

90 % face-to-face, 10 % distance learning

Prerequisites

basic mathematics

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Egermayer,F.-Boháč,M.:Statistika pro techniky, SNTL,1984
J. Anděl: Statistické metody, , 0
A. Linczenyi: Inžinierska štatistika, , 0
Bakytová,H.: Základy štatistiky, ALFA, 1975
Montgomery, D.C.: Introduction to Statistical Quality Control, John Wiley&Sons, Inc., 2001

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.

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

 

Lecture

26 hours, 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.

seminars in computer labs

13 hours, 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.