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

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

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