Course detail

Experiment, Measurement and Statistics

FSI-ZESAcad. year: 2026/2027

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.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Michal Kubík, Ph.D.

Department

Institute of Machine and Industrial Design (ÚK)

Entry knowledge

Knowledge of mathematics, computer science, and electrical engineering.

Rules for evaluation and completion of the course

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.

Aims

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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

HOLMAN, J.P. Experimental methods for engineers. 7th ed. Boston: McGraw-Hill, 2001. ISBN 0-07-118165-2. (EN)
KARPÍŠEK, Zdeněk. Matematika IV: statistika a pravděpodobnost. 4., přeprac. Vyd. Brno: Akademické nakladatelství CERM, 2014. ISBN 978-80-214-4858-2. (CS)
MARTINEK, Radislav. Senzory v průmyslové praxi. Praha: BEN – technická literatura, 2004. ISBN 80-7300-114-4. (CS)
MONTGOMERY, Douglas C. a George C. RUNGER. Applied statistics and probability for engineers. Seventh edition. New York: Wiley, [2018]. Emea edition. ISBN 978-1-119-58559-6.Začátek formuláře (EN)

Recommended reading

ANDĚL, Jiří. Základy matematické statistiky. Vyd. 3. Praha: Matfyzpress, 2011. ISBN 978-80-7378-162-0. (CS)
JANÍČEK, Přemysl. Technický experiment. Brno: Ediční středisko VUT, 1989. Učební texty vysokých škol (Vysoké učení technické v Brně). ISBN 80-214-1011-6. (CS)
KIRKUP, Les. Experimental methods for science and engineering students: an introduction to the analysis and presentation of data. Second edition. Cambridge: Cambridge University Press, [2019]. ISBN 978-1-108-41846-1. (EN)
KREIDL, Marcel a Radislav ŠMÍD. Technická diagnostika: senzory, metody, analýza signálu. Praha: BEN - technická literatura, 2006. Senzory neelektrických veličin. ISBN 80-7300-158-6. (CS)

Classification of course in study plans

  • Programme B-KSI-P Bachelor's 3 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Michal Kubík, Ph.D.

Syllabus

  • 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.

Laboratory exercise

14 hod., compulsory

Teacher / Lecturer

doc. Ing. Michal Kubík, Ph.D.

Syllabus

  • 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.

Computer-assisted exercise

12 hod., compulsory

Teacher / Lecturer

doc. Ing. Michal Kubík, Ph.D.

Syllabus

  • 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.