Course detail
Statistical Methods in Engineering
FSI-PST Acad. year: 2019/2020 Summer semester
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
Supervisor
Department
Learning outcomes of the course unit
Data analysis, descriptive statistics, sample, population, testing hypothesis
Prerequisites
basic mathematics
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
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
The study programmes with the given course
Programme B3A-P: Applied Sciences in Engineering, Bachelor's
branch B-MAI: Mathematical Engineering, elective (voluntary)
Programme B3S-P: Engineering, Bachelor's
branch B-STI: Fundamentals of Mechanical Engineering, elective (voluntary)
Programme M2I-P: Mechanical Engineering, Master's
branch M-SLE: Foundry Technology, compulsory-optional
Programme M2I-P: Mechanical Engineering, Master's
branch M-STM: Manufacturing Technology and Management in Industry, compulsory
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.
Computer-assisted exercise
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.