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Presentation ofstatistical data
Published in John Bird, Bird's Basic Engineering Mathematics, 2021
Statistics is the study of the collection, organisation, analysis and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments. Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, engineering, government and business. Statistical methods can be used for summarising or describing a collection of data. Engineering statistics combines engineering and statistics. Design of experiments is a methodology for formulating scientific and engineering problems using statistical models. Quality control and process control use statistics as a tool to manage conformance to specifications of manufacturing processes and their products. Time and methods engineering use statistics to study repetitive operations in manufacturing in order to set standards and find optimum manufacturing procedures. Reliability engineering measures the ability of a system to perform for its intended function (and time) and has tools for improving performance. Probabilistic design involves the use of probability in product and system design. System identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models. This chapter introduces the presentation of statistical data.
Presentation of statistical data
Published in John Bird, Basic Engineering Mathematics, 2017
Statistics is the study of the collection, organisation, analysis and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments. Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, engineering, government and business. Statistical methods can be used for summarising or describing a collection of data. Engineering statistics combines engineering and statistics. Design of experiments is a methodology for formulating scientific and engineering problems using statistical models. Quality control and process control use statistics as a tool to manage conformance to specifications of manufacturing processes and their products. Time and methods engineering use statistics to study repetitive operations in manufacturing in order to set standards and find optimum manufacturing procedures. Reliability engineering measures the ability of a system to perform for its intended function (and time) and has tools for improving performance. Probabilistic design involves the use of probability in product and system design. System identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models. This chapter introduces the presentation of statistical data.
Presentation of statistical data
Published in John Bird, Engineering Mathematics, 2017
Statistics is the study of the collection, organisation, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments. Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, engineering, government, and business. Statistical methods can be used for summarising or describing a collection of data. Engineering statistics combines engineering and statistics. Design of experiments is a methodology for formulating scientific and engineering problems using statistical models. Quality control and process control use statistics as a tool to manage conformance to specifications of manufacturing processes and their products. Time and methods engineering use statistics to study repetitive operations in manufacturing in order to set standards and find optimum manufacturing procedures. Reliability engineering measures the ability of a system to perform for its intended function (and time) and has tools for improving performance. Probabilistic design involves the use of probability in product and system design. System identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models. This chapter introduces the presentation of statistical data.
Introduction to special edition of Quality Engineering
Published in Quality Engineering, 2022
Roger W. Hoerl, Ronald D. Snee
Before discussing the individual articles, we should provide a brief introduction to SE for those who may not be familiar with this discipline. First of all, in the phrase “statistical engineering,” “engineering” is the noun, that is, the “what.” “Statistical” is the adjective, which modifies that noun, i.e., explains the type of engineering. In other words, SE is a form of engineering, one in which statistics plays a heavy role. Of course, nouns and adjectives cannot be reversed without changing the meaning of the phrase. Therefore, just as “data science” is not the same thing as “scientific data,” “engineering statistics” is not the same thing as SE. Engineering statistics refers to the application of statistics to engineering problems, while SE refers to the engineering of solutions to complex statistical problems, which might be in healthcare, finance, education, or any other application area.
Fuzzy C-Means Clustering Applied to Load Profiling of Industrial Customers
Published in Electric Power Components and Systems, 2022
Adisa Dedić, Tatjana Konjić, Martin Ćalasan, Zehrudin Dedić
In the available literature, load profiling use approaches based on engineering, statistics, and artificial intelligence.The engineering approach uses bottom-up (such as statistical random models, probabilistic empirical models or time of use-based models), top-down (deterministic statistical disaggregation models) or hybrid variant (statistical engineering models) techniques for load modeling [19].The statistical approach characterizes electricity use (load profile vs. time) based on statistics and probability analysis. These approaches are specific as they involve electric appliances, or lifestyle, or activity patterns and similar [20–21].The artificial intelligence uses different algorithms for creating load profiles and load classification or load predicting. Different approaches based on the usage of neural networks, fuzzy logic and data mining also belong to this group [22–24].
Integrating entrepreneurial activities in chemical engineering education: a case study on solid waste management
Published in European Journal of Engineering Education, 2020
Project-based learning is the other most important method to enhance the entrepreneurial mindset in students. Some researchers have used this learning strategy in their courses. For example, Vignola et al. (2017) and Hurajova, Firsova, and Nikolaevna Glukhova (2018) used project-based learning to demonstrate how this learning method can be integrated into engineering courses with the aim of investigating its impact on students’ entrepreneurial spirit. Vignola et al. (2017) performed their study in an undergraduate engineering statistics course. Students distributed in six teams worked on the challenge of ‘A World Without Statistics’. Findings of the study showed that project-based learning has the potential to increase the entrepreneurial mindset. Hurajova, Firsova, and Nikolaevna Glukhova (2018) based their study on the students of Master degree. For two years they performed a new approach in class sessions to enhance entrepreneurial mindset of their students. In their work, authors exposed the main advantages and disadvantages of the new model of teaching.