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Lecturer(s)
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Urban Jan, Ing. Ph.D.
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Císař Petr, Ing. Ph.D.
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Course content
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The aim of the course is to introduce students to the method of statistical design of the experiment and try to think about the whole process of experimentation from design to experiment to the evaluation. Students will learn the most common concepts of measuring theory, sources of error measurement, their promotion and interpretation. On a model example of a paper helicopter, they will compare the standard methods of design and implementation of experiment and of statistical design and evaluation. Within the subject the students recall the basic knowledge from the theory of measurement, statistics and probability on which the whole subject is built. The prerequisite of the course is basic knowledge of probability and statistics. The methods explained in the course build on hypothesis testing (ANOVA, T-Test, F-Test), linear regressions and normal distribution. All concepts are re-discussed within the subject. The aim of the course is to introduce students to the theory of probability and basic statistical concepts, including their usefulness and methods of experimental design. Make the student think about the whole process of experiment (experiment, experiment, evaluation of the experiment), to acquaint the student with the basic methods of statistical experiment design and to verify theoretical knowledge on a real example in the form of a competition. This part will be preceded by familiarization with the most common concepts of measurement theory, their interpretation and knowledge of error measurement and error propagation. The subject is divided into five logical units: Basic concepts, Theory of measurement, Representation of numbers, What is an experiment, DOE theory, Practical example of experimental statistical design. The topics of the lectures will be as follows: 1) Understand the most common concepts of measurement theory a. Probability theory b. Basic statistical concepts c. Theory of measurement d. Attributes of measurement e. Repeatability and reproducibility f. Promotion of errors 2) The representation of numbers in the computer and the practical implications resulting from the processing and analysis of the data a. Digital representation of numbers b. Sampling with quantization 3) Experimental Technique on a Specific Example. The weaknesses and strengths of each approach and an overview of the basic techniques of evaluating experimental data a. Introduction to statistical experimental design b. Classical methods of experiment design 4) DOE theoretical basis. The terminology used, followed by an explanation of all parts of the DOE, will be presented: problem analysis, factor selection, full factorial, experimental data, data analysis, other experiment suggestions (Taguchi, optimal design). This part of the course will be supported by one of the DOE software tools (Unscrambler) and all the theories will be demonstrated on examples. Within the whole, an ANOVA method will be explained so that students can handle calculations without specialized software. a. Terminology DOE b. Statistical experiment design c. ANOVA explanation of the calculation in a practical example 5) Practice theoretical knowledge on a practical example. A practical example will be a paper helicopter design or a golf machine design. a. Preparing a Practical Example b. Analysis of the results from the practical example Study materials in the form of lectures and video lectures are available in Stag on the book of study materials after each lecture.
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Learning activities and teaching methods
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Dialogic (discussion, interview, brainstorming), Projection, Activating (simulations, games, drama)
- Class attendance
- 52 hours per semester
- Semestral paper
- 40 hours per semester
- Preparation for exam
- 20 hours per semester
- Preparation for credit
- 20 hours per semester
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Learning outcomes
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The aim of the course is to introduce students to the method of statistical design of the experiment and try to think about the whole process of experimentation from design to experiment to the evaluation. Students will learn the most common concepts of measuring theory, sources of error measurement, their promotion and interpretation. On a model example of a paper helicopter, they will compare the standard methods of design and implementation of experiment and of statistical design and evaluation. Within the subject the students recall the basic knowledge from the theory of measurement, statistics and probability on which the whole subject is built.
Graduates will be able to design, implement and evaluate the experiment using statistical methods. They will be able to use the software tools for so called Design of experiment.
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Prerequisites
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The prerequisite of the course is basic knowledge of probability and statistics. The methods explained in the course build on hypothesis testing (ANOVA, T-Test, F-Test), linear regressions and normal distribution. All concepts are re-discussed within the subject.
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Assessment methods and criteria
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Combined exam, Development of laboratory protocols
Basic knowledge of statistics and probabilities. The subject assumes orientation in hypothesis testing, mean value and scattering, and awareness of normal distribution.
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Recommended literature
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Antony Jiju. Design of Experiments for Engineers and Scientists. 2010. ISBN 9780750647090.
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Paul D Berger, Robert E. Maurer. Experimental design with applications in management, engineering, and the sciences. 2018. ISBN 978-3-319-64582-7.
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