Exploratory Data Analysis in Quality-Improvement Projects
| Autor: | Jeroen de Mast, Albert Trip |
|---|---|
| Přispěvatelé: | Stochastics (KDV, FNWI) |
| Rok vydání: | 2007 |
| Předmět: |
Quality management
Strategy and Management 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Machine learning computer.software_genre 01 natural sciences Industrial and Manufacturing Engineering 010104 statistics & probability Data visualization Econometrics Entropy (information theory) 0101 mathematics Safety Risk Reliability and Quality Mathematics 021103 operations research business.industry Principle of maximum entropy Probabilistic logic Exploratory data analysis Salient Artificial intelligence business Quality assurance computer |
| Zdroj: | Journal of Quality Technology, 39(4), 301-311. American Society for Quality |
| ISSN: | 2575-6230 0022-4065 |
| Popis: | The aim of this paper is to provide a prescriptive framework for exploratory data analysis (EDA) in quality improvement projects. The framework is developed on the basis of a large number of real-life applications. The three steps of EDA are described: display the data, identify salient features, and interpret salient features. Graphical display of data, Shewhart's assignable causes, the maximum entropy principle, abduction and explanatory coherence all are part of the resulting framework. Furthermore, the roles of probabilistic reasoning and automatic statistical procedures in EDA are discussed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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