Course detail

Applied Statistics and Design of Experiments

FSI-XAPAcad. year: 2019/2020

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.

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.


The knowledge of probability theory and basic statistics is assumed.


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Montgomery, D. C. - Renger, G.: Applied Statistics and Probability for Engineers. New York : John Wiley & Sons, 2003.
Anděl, J.: Statistické metody. 2. vyd. Praha: MATFYZPRESS, 2003. (CS)
Meloun, M. - Militký, J.: Statistické zpracování experimentálních dat. Praha: PLUS, 1994. (CS)
Hahn, G. J. - Shapiro, S. S.: Statistical Models in Engineering.New York : John Wiley & Sons, 1994.

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.

Language of instruction


Work placements

Not applicable.


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.

Classification of course in study plans

  • Programme M2I-P Master's

    branch M-KSB , 1. year of study, winter semester, 4 credits, compulsory

Type of course unit



26 hours, optionally

Teacher / Lecturer


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

Computer-assisted exercise

13 hours, compulsory

Teacher / Lecturer


1. Random generator of software MATHCAD (STATISTICA).
2. Examples of common and special causes.
3. Normal distribution in engineering subjects.
4. Probability density functions and probability distributions.
5. Computation of distributions of averages.
6. Basic assumptions for different types of control charts.
7. Confidence intervals for different sizes of samples.
8. Hypothesis testing.
9. Grubbs and Dixon tests.
10. Linear regression model.
11. Factorial experiment.
12. Orthogonal designs, full and fractioanal design.
13. Process optimization with design experiment.