Course detail
Applied Statistics and Design of Experiments
FSI-XAP-K Acad. year: 2019/2020 Winter semester
Students sometimes use statistics to describe the results of an experiment or an investigation. This process is referred to as data analysis or descriptive statistics. Technicians also use another way; if the entire population of interest is not accessible to them for some reason, they often observe only a portion of the population (a sample) and use statistics to answer questions about the whole population. This process is called inferential statistics. Statistical inference is the main focus of the course.
Language of instruction
Czech
Number of ECTS credits
4
Supervisor
Department
Learning outcomes of the course unit
Populations, samples, binomial and Poisson distributions, distribution of averages, distribution of a continuous probability, confidence intervals, testing of hypotheses, regression analysis, design of experiments.
Prerequisites
The knowledge of probability theory and basic statistics is assumed.
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
Exam has a written and an oral part.
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
Missed lessons may be compensated for via a written test.
The study programmes with the given course
Programme M2I-K: Mechanical Engineering, Master's
branch M-KSB: Quality, Reliability and Safety, compulsory
Type of course unit
Guided consultation in combined form of studies
13 hours, optionally
Teacher / Lecturer
Syllabus
1. Collection of observations.
2. Common and special causes of variation.
3. Normal distribution in engineering subjects.
4. Distributions of averages.
5. Basic assumptions for different types of control charts.
6. Confidence intervals.
7. Hypothesis testing.
8. Outliers.
9. Correlation.
10. Linear regression model.
11. Factorial experiment, orthogonal designs.
12. Full and fractioanal design.
13. Process optimization with design experiment