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Lecturer(s)
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Pata Vladimír, prof. Dr. Ing.
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Course content
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- The nature of one and more dimensional data, structured and unstructured data. - Methods of pre-treatment of one and more dimensional data, forms of data standardization, use of statistical weights. - Methodologies of representation of structure in individual characters and objects. - Analysis of principal components, essence, graphical expression, Cattel's graph at the foot of eigenvalues, diagnostics of principal methods component. - Factor analysis, focus, essence, graphic aids, diagnostics and use of obtained results. - Canonical correlation analysis, nature and focus, significance tests, redundancy analysis, task formulation and testing of results. - Linear discriminant analysis, quadratic discriminant analysis, purpose of formulation, use in research. Linear and quadratic discriminant functions, testing results. - Logistic regression, purpose and use in science and research, creation of logistic regression model, significance tests and evaluation quality of logistic regression. - Cluster analysis, formulation and use, types of dendrograms, types of clustering, finding optimal clusters. - Introduction to the issue of "fuzzy clustering". - Mapping of objects using multidimensional scaling, goals and uses, methodology and interpretation of results, including their verification. - Correspondence analysis, principle and use in scientific practice, focus of correspondence analysis, formulation and interpretation of results. - Nonlinear regression analysis, purpose and use, formulation of problems, finding the best nonlinear regression function, confidence intervals for nonlinear regression analysis. - Neural networks, types, uses, formulation of problems and own mathematical apparatus. Neural network learning and testing.
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Learning activities and teaching methods
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Lecturing, Methods for working with texts (Textbook, book), Demonstration, Exercises on PC, Teamwork, Individual work of students
- Preparation for course credit
- 120 hours per semester
- Preparation for examination
- 50 hours per semester
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| prerequisite |
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| Knowledge |
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| Knowledge of mathematics. |
| Knowledge of mathematics. |
| In order to successfully master the subject, it is assumed that the student has certain professional knowledge and skills before the start of the course. This assumed knowledge and skills are important for the effective study and application of computer-based methods of statistics. Here are some of those expertise and skills: Basic Statistical Knowledge: The student should have basic knowledge of statistical concepts such as probability, mean, variance, correlation, regression and probability distribution. These concepts are the basis for advanced computational methods of statistics. Mathematical skills: Basic mathematical skills are important because statistical methods often involve mathematical calculations and algebra for data analysis. Computer Skills: The student should be able to work with a computer and have basic skills in using statistical software tools such as R, Python with statistics libraries, SPSS, Minitab or others. Data Analysis Skills: The student should have data analysis skills, including the ability to evaluate and visualize data and identify patterns and trends in data. Working with databases: Knowledge of the basic principles of working with databases can be useful, as statistical analysis often involves working with large data sets. Knowledge of programming languages: Students who have programming experience may have an advantage as they may be able to create their own scripts to analyze data and apply statistical methods. Knowledge of mathematical statistical models: Advanced statistical models may include knowledge of linear regression, nonlinear regression, analysis of variance (ANOVA), factor analysis, principal component analysis (PCA), and more. Skills in interpreting results: The student should be able to interpret the results of statistical analyzes and explain significant statistical findings. With these knowledge and skills, the student is prepared to study computer methods of statistics and acquire advanced knowledge and skills in the field of data analysis and interpretation. After completing the course, the student will be able to apply statistical methods to various real problems and perform statistical analyzes using computer tools. |
| In order to successfully master the subject, it is assumed that the student has certain professional knowledge and skills before the start of the course. This assumed knowledge and skills are important for the effective study and application of computer-based methods of statistics. Here are some of those expertise and skills: Basic Statistical Knowledge: The student should have basic knowledge of statistical concepts such as probability, mean, variance, correlation, regression and probability distribution. These concepts are the basis for advanced computational methods of statistics. Mathematical skills: Basic mathematical skills are important because statistical methods often involve mathematical calculations and algebra for data analysis. Computer Skills: The student should be able to work with a computer and have basic skills in using statistical software tools such as R, Python with statistics libraries, SPSS, Minitab or others. Data Analysis Skills: The student should have data analysis skills, including the ability to evaluate and visualize data and identify patterns and trends in data. Working with databases: Knowledge of the basic principles of working with databases can be useful, as statistical analysis often involves working with large data sets. Knowledge of programming languages: Students who have programming experience may have an advantage as they may be able to create their own scripts to analyze data and apply statistical methods. Knowledge of mathematical statistical models: Advanced statistical models may include knowledge of linear regression, nonlinear regression, analysis of variance (ANOVA), factor analysis, principal component analysis (PCA), and more. Skills in interpreting results: The student should be able to interpret the results of statistical analyzes and explain significant statistical findings. With these knowledge and skills, the student is prepared to study computer methods of statistics and acquire advanced knowledge and skills in the field of data analysis and interpretation. After completing the course, the student will be able to apply statistical methods to various real problems and perform statistical analyzes using computer tools. |
| Skills |
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| In order to successfully master the subject "computer methods of statistics", it is assumed that the student has certain professional skills and abilities before the start of the course. These professional skills are important for the effective study and application of computer-based methods of statistics. Here are the expected professional skills that a student should have: Computer Skills: The student should be computer proficient and comfortable working with various statistical software tools such as R, Python, SPSS, Minitab or others. Data processing and analysis: The student should have skills in data processing, including the ability to import, transform and clean data for statistical analysis. This also includes the ability to visualize data using charts and tables. Use of statistical software tools: The student should be able to effectively use various statistical software tools and know the syntax and commands used to perform statistical analyses. Statistical Data Analysis: The student should have skills in performing various statistical analysis such as regression, analysis of variance (ANOVA), hypothesis testing, correlation analysis and others. The skills of choosing the right statistical procedure are key. Data Modeling: The student should be able to create statistical models for data and perform analysis to predict and estimate future values. Working with large data sets: Skills in working with large data sets and the ability to efficiently perform analysis on large amounts of data are important in the modern statistical environment. Interpretation of results: The student should be able to interpret the results of statistical analyzes and explain significant statistical findings in terms of real applications. Critical thinking: Critical thinking and the ability to assess the quality of data, the appropriateness of statistical methods and the interpretation of results are important. Working with real data: The student should have experience working with real data and performing statistical analyzes on real problems. These skills prepare the student for the study and application of computational methods of statistics, which includes the analysis and interpretation of data, and enables the student to successfully solve a variety of statistical problems in a variety of fields, including scientific research, industry, the financial sector, and others. |
| In order to successfully master the subject "computer methods of statistics", it is assumed that the student has certain professional skills and abilities before the start of the course. These professional skills are important for the effective study and application of computer-based methods of statistics. Here are the expected professional skills that a student should have: Computer Skills: The student should be computer proficient and comfortable working with various statistical software tools such as R, Python, SPSS, Minitab or others. Data processing and analysis: The student should have skills in data processing, including the ability to import, transform and clean data for statistical analysis. This also includes the ability to visualize data using charts and tables. Use of statistical software tools: The student should be able to effectively use various statistical software tools and know the syntax and commands used to perform statistical analyses. Statistical Data Analysis: The student should have skills in performing various statistical analysis such as regression, analysis of variance (ANOVA), hypothesis testing, correlation analysis and others. The skills of choosing the right statistical procedure are key. Data Modeling: The student should be able to create statistical models for data and perform analysis to predict and estimate future values. Working with large data sets: Skills in working with large data sets and the ability to efficiently perform analysis on large amounts of data are important in the modern statistical environment. Interpretation of results: The student should be able to interpret the results of statistical analyzes and explain significant statistical findings in terms of real applications. Critical thinking: Critical thinking and the ability to assess the quality of data, the appropriateness of statistical methods and the interpretation of results are important. Working with real data: The student should have experience working with real data and performing statistical analyzes on real problems. These skills prepare the student for the study and application of computational methods of statistics, which includes the analysis and interpretation of data, and enables the student to successfully solve a variety of statistical problems in a variety of fields, including scientific research, industry, the financial sector, and others. |
| learning outcomes |
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| Knowledge |
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| from the field of metrology and quality systems |
| from the field of metrology and quality systems |
| explain in your own words the basic methods belonging to metrology |
| explain in your own words the basic methods belonging to metrology |
| list and explain measurement processes and methods |
| list and explain measurement processes and methods |
| formulate and apply quality assurance tools and methods |
| formulate and apply quality assurance tools and methods |
| After completing the subject "computer methods of experiment planning", the student demonstrates the following professional knowledge: Design of experiments: The student has a deeper understanding of the basic principles of design of experiments, including selection of appropriate factors and levels, design of experimental design, and strategies for obtaining relevant data. |
| After completing the subject "computer methods of experiment planning", the student demonstrates the following professional knowledge: Design of experiments: The student has a deeper understanding of the basic principles of design of experiments, including selection of appropriate factors and levels, design of experimental design, and strategies for obtaining relevant data. |
| Statistical methods: The student has knowledge of various statistical methods used in planning experiments, including analysis of variance (ANOVA), regression, the method of optimal design of experiment, and others. |
| Statistical methods: The student has knowledge of various statistical methods used in planning experiments, including analysis of variance (ANOVA), regression, the method of optimal design of experiment, and others. |
| Design of experiments: The student has a deeper understanding of the basic principles of design of experiments, including selection of appropriate factors and levels, design of experimental design, and strategies for obtaining relevant data. |
| Design of experiments: The student has a deeper understanding of the basic principles of design of experiments, including selection of appropriate factors and levels, design of experimental design, and strategies for obtaining relevant data. |
| Data analysis: The student understands methods of data analysis from experiments, including interpretation of results, identification of the influence of factors and optimization of processes based on the information obtained. |
| Data analysis: The student understands methods of data analysis from experiments, including interpretation of results, identification of the influence of factors and optimization of processes based on the information obtained. |
| Software for design of experiments: The student is proficient in the use of special software for design of experiments, which may include software such as Minitab, Design-Expert, JMP and others. |
| Software for design of experiments: The student is proficient in the use of special software for design of experiments, which may include software such as Minitab, Design-Expert, JMP and others. |
| Critical thinking: The student is able to critically assess the results of experiments, the findings and the ways in which the data were obtained, and can suggest improvements to the experimental design. |
| Critical thinking: The student is able to critically assess the results of experiments, the findings and the ways in which the data were obtained, and can suggest improvements to the experimental design. |
| Application in practice: The student is able to apply his knowledge of planning experiments in real situations and solve problems in an industrial, scientific or engineering context. |
| Application in practice: The student is able to apply his knowledge of planning experiments in real situations and solve problems in an industrial, scientific or engineering context. |
| Statistical validity: The student understands the basic principles of statistical validity and ways to prevent distortion of experimental results. |
| Statistical validity: The student understands the basic principles of statistical validity and ways to prevent distortion of experimental results. |
| Criteria for evaluating the success of the experiment: The student has knowledge of the criteria for evaluating the success of the experiment and the ability to assess whether the required goals have been met. |
| Criteria for evaluating the success of the experiment: The student has knowledge of the criteria for evaluating the success of the experiment and the ability to assess whether the required goals have been met. |
| Ethics and standards: The student is familiar with the ethical and regulatory aspects related to the planning and execution of experiments, including the protection of personal data and compliance with standards and regulations. |
| Ethics and standards: The student is familiar with the ethical and regulatory aspects related to the planning and execution of experiments, including the protection of personal data and compliance with standards and regulations. |
| This expertise enables the student to effectively plan, perform and analyze experiments in order to optimize processes, improve quality and achieve specific goals. After completing the course, the student is able to apply experiment planning methods in various fields, including research and development, industrial engineering, the pharmaceutical industry, and others. |
| This expertise enables the student to effectively plan, perform and analyze experiments in order to optimize processes, improve quality and achieve specific goals. After completing the course, the student is able to apply experiment planning methods in various fields, including research and development, industrial engineering, the pharmaceutical industry, and others. |
| Skills |
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| describe the historical development of metrology in your own words |
| describe the historical development of metrology in your own words |
| in the division of types of metrology and determination of its goals |
| in the division of types of metrology and determination of its goals |
| in the application of basic evaluation methods of metrology |
| in the application of basic evaluation methods of metrology |
| After completing the subject "computer methods of experiment planning", the student demonstrates the following professional skills: Planning experiments: The student has the ability to plan experiments, including the selection of factors, their levels and the design of an experimental plan in order to achieve the specific goals of the experiment. |
| After completing the subject "computer methods of experiment planning", the student demonstrates the following professional skills: Planning experiments: The student has the ability to plan experiments, including the selection of factors, their levels and the design of an experimental plan in order to achieve the specific goals of the experiment. |
| Selection of appropriate statistical methods: The student is able to select appropriate statistical methods for analyzing data from experiments, including analysis of variance (ANOVA), regression, the method of optimal design of experiment (DOE) and others. |
| Selection of appropriate statistical methods: The student is able to select appropriate statistical methods for analyzing data from experiments, including analysis of variance (ANOVA), regression, the method of optimal design of experiment (DOE) and others. |
| Work with experimental data: The student has the skill to collect experimental data and process it in accordance with the experiment plan. This includes the ability to take measurements and record data accurately and reliably. |
| Work with experimental data: The student has the skill to collect experimental data and process it in accordance with the experiment plan. This includes the ability to take measurements and record data accurately and reliably. |
| Interpretation of results: The student is able to interpret the results of the experiment and identify the influence of factors on the investigated process or system. |
| Interpretation of results: The student is able to interpret the results of the experiment and identify the influence of factors on the investigated process or system. |
| Process optimization: The student has the ability to perform process optimization based on findings from experiments and propose changes and improvements. |
| Process optimization: The student has the ability to perform process optimization based on findings from experiments and propose changes and improvements. |
| Use of statistical software: The student is able to effectively use statistical software to plan and analyze experiments, which includes knowledge of the syntax, commands, and functions used in the software. |
| Use of statistical software: The student is able to effectively use statistical software to plan and analyze experiments, which includes knowledge of the syntax, commands, and functions used in the software. |
| Critical thinking: The student is able to critically assess experimental procedures, results and conclusions and perform quality control of experiments. |
| Critical thinking: The student is able to critically assess experimental procedures, results and conclusions and perform quality control of experiments. |
| Practical application: The student has the ability to apply their experiment planning skills in a variety of applications, including scientific research, industrial engineering, pharmacy, product development and other fields. |
| Practical application: The student has the ability to apply their experiment planning skills in a variety of applications, including scientific research, industrial engineering, pharmacy, product development and other fields. |
| Working with a team: The student can work in a team and communicate with colleagues and experts to jointly design, perform and analyze experiments. |
| Working with a team: The student can work in a team and communicate with colleagues and experts to jointly design, perform and analyze experiments. |
| Ethical issues: The student is familiar with the ethical aspects related to the planning and execution of experiments, including the protection of personal data and compliance with ethical and regulatory standards and regulations. |
| Ethical issues: The student is familiar with the ethical aspects related to the planning and execution of experiments, including the protection of personal data and compliance with ethical and regulatory standards and regulations. |
| These professional skills enable the student to effectively plan, perform and analyze experiments in order to optimize processes, improve product quality and achieve set goals in various professional and research contexts. After completing the course, the student will be able to use computer methods of experiment planning as a tool for effective solution of real problems and innovation in industry and science. |
| These professional skills enable the student to effectively plan, perform and analyze experiments in order to optimize processes, improve product quality and achieve set goals in various professional and research contexts. After completing the course, the student will be able to use computer methods of experiment planning as a tool for effective solution of real problems and innovation in industry and science. |
| teaching methods |
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| Knowledge |
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| Lecturing |
| Lecturing |
| Demonstration |
| Demonstration |
| Methods for working with texts (Textbook, book) |
| Methods for working with texts (Textbook, book) |
| Exercises on PC |
| Exercises on PC |
| Individual work of students |
| Individual work of students |
| Skills |
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| Exercises on PC |
| Exercises on PC |
| Teamwork |
| Teamwork |
| Analysis of a presentation |
| Analysis of a presentation |
| assessment methods |
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| Knowledge |
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| Grade (Using a grade system) |
| Analysis of educational material |
| Written examination |
| Grade (Using a grade system) |
| Analysis of the student's performance |
| Oral examination |
| Oral examination |
| Analysis of educational material |
| Written examination |
| Analysis of the student's performance |
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Recommended literature
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Bumbálek, L. a kol. Kontrola a měření. Praha: Informatorium, spol. s r.o., 2009. ISBN 978-80-7333-072-9.
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David J. Whitehouse. Handbook of Surface and Nanometrology, Second Edition 2nd Edition, Kindle Edition. 2010 Amazon Media EU.
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Hendl, J. Přehled statistických metod zpracování dat. Praha: Portál, 2004.
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Kjell, J. Optical Metrology. England: John Willey and Sons, 2003. ISBN 0470843004.
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Meloun, Milan. Statistická analýza experimentálních dat. Vyd. 2., upr. a rozš. Praha : Academia, 2004. ISBN 80-200-1254-0.
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Nenadál, Jaroslav. Moderní systémy řízení jakosti : quality management. 2. dopl. vyd. Praha : Management Press, 2005. ISBN 8072610716.
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Nenáhlo, Č. Měření vybraných geometrických veličin. Praha: Česká metrologická společnost, 2005.
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Pernikář, J., Tykal, M., Vačkář, J. Jakost a metrologie. Brno: VUT Brno, 2001. ISBN 80-214-1997-0.
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Smith, Graham T. Industrial Metrology. 2002. ISBN 978-1-85233-507-6.
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