Course detail

# Applied Statistics and Design of Experiments

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

3

Mode of study

Not applicable.

Entry knowledge

The knowledge of probability theory and basic statistics is assumed.

Rules for evaluation and completion of the course

Exam has a written and an oral part.
Missed lessons may be compensated for via a written test.

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.
Populations, samples, binomial and Poisson distributions, distribution of averages, distribution of a continuous probability, confidence intervals, testing of hypotheses, regression analysis, design of experiments.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

J. Anděl: Matematická statistika, SNTL/ALFA, Praha 1978

Montgomery, D. C. - Renger, G.: Applied Statistics and Probability for Engineers. New York : John Wiley & Sons, 2003.
Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering.New York : John Wiley & Sons, 1994.

Classification of course in study plans

• Programme N-KSB-K Master's, 1. year of study, winter semester, compulsory

#### Type of course unit

Guided consultation in combined form of studies

13 hours, compulsory

Teacher / Lecturer

Syllabus

1. Collection of observations, Common and special causes of variation.
2. Normal distribution in engineering subjects, Distributions of averages.
3. Basic assumptions for different types of control charts.
4. Confidence intervals.
5. Hypothesis testing I.
6. Hypothesis testing II.
7. Correlation.
8. Linear regression model.
9. Introduction to the Design of Experiment
10. Factorial experiment, orthogonal designs.
11. Full and fractioanal design.
12. Response surfaces
13. Process optimization with design experiment

Guided consultation

26 hours, optionally

Teacher / Lecturer

Syllabus

1. Introduction to Minitab statistical software
2. Normal distribution in engineering subjects, Distributions of averages.
3. Basic assumptions for different types of control charts.
4. Confidence intervals.
5. Hypothesis testing I.
6. Hypothesis testing II.
7. Correlation.
8. Linear regression model.
9. Introduction to the Design of Experiment
10. Factorial experiment, orthogonal designs.
11. Full and fractioanal design.
12. Response surfaces
13. Process optimization with design experiment