| 书目名称 | Empirical Modeling and Data Analysis for Engineers and Applied Scientists | | 编辑 | Scott A. Pardo | | 视频video | | | 概述 | Focuses on methods that can be used with "small" data sets, generally gathered in designed experiments.Encourages students to use data intrinsically meaningful to him or her, such as experimental data | | 图书封面 |  | | 描述 | This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions..While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. .That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or processactually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in | | 出版日期 | Textbook 2016 | | 关键词 | statistics; methods; regression; analysis; models; modeling; empirical; data; engineering; applied scientist; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-32768-6 | | isbn_softcover | 978-3-319-81364-6 | | isbn_ebook | 978-3-319-32768-6 | | copyright | Springer International Publishing Switzerland 2016 |
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发表于 2025-3-21 17:43:29
