Experiment, Measurement and Statistics (FSI-ZES)

Graduates will be able to independently design and assemble a simple measurement chain and will be familiar with measurement devices and sensor technology. They will acquire fundamental knowledge of probability theory and will be able to apply this knowledge in the appropriate statistical processing of measured data.

• Knowledge of basic methods for statistical processing of experimental data (descriptive statistics, hypothesis testing, linear regression).
• Ability to build and implement a simple measurement chain and calibrate selected sensors.
• Ability to select an appropriate sensor for a given application, considering measurement accuracy, installation dimensions, cost, etc.
• Ability to assess the relevance of measured data and quantify measurement error.
• Ability to present measured data and the conclusions of their statistical analysis.

Knowledge of mathematics, computer science, and electrical engineering.

The course introduces students to the methods and principles of technical experimentation, including statistical data processing. It provides an overview of measurement methods for kinematic quantities, forces, torques, and pressures, as well as signal analysis in both the time and frequency domains. Particular attention is given to understanding the fundamental properties of probability, their implications for statistical data processing, and the subsequent presentation of results. The course integrates knowledge gained in theoretical subjects such as mathematics and computer science.

Conditions for obtaining course credit (0–100 points, minimum required for credit is 50):

• submission of all laboratory exercise reports at an appropriate technical and graphical level (minimum 50 out of 100 points).

 Conditions for passing the exam (0–100 points, minimum required to pass is 50):

• statistical analysis of the assigned data set (minimum 25 out of 50 points),
• completion of an assigned experimental task (minimum 25 out of 50 points).

A total of up to 100 points can be achieved, and the final grade is determined according to the ECTS grading scale.

Lectures: attendance is recommended.

Exercises: attendance is mandatory and monitored by the instructor; a maximum of two absences is permitted. In case of long-term absence, arrangements for make-up classes are at the discretion of the course guarantor.

• Descriptive statistics.


• Fundamentals of probability (random variable, characteristics of a random variable, independence of events).


• Probability distributions for applications.


• Random sampling. Hypothesis testing.


• Linear regression and correlation.


• Theory of experiment, experimental chain, basic parameters of sensors and measuring devices.


• Signal processing in the time and frequency domains, filtering.


• Measurement of basic quantities in solid mechanics.


• Fundamentals of technical diagnostics. Acoustic measurements in mechanical engineering.

• Properties of measuring devices (resolution, sampling frequency, etc.)


• Fourier transform in practice and basics of filtering.


• Measurement of acceleration, position, and velocity.


• Measurement of force, pressure, and electrical quantities.


• Pulse sensors and temperature sensors.


• Acoustic measurements.

• Fundamentals of probability (random variable, characteristics of a random variable, independence of events).


• Hypothesis testing.


• Linear regression.


• Statistical processing of experimental data and presentation of results.

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