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Empirical Modeling and Data Analysis for Engineers and Applied Scientists by Scott A. Pardo-P2P

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 process actually 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 the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes.

Empirical Modeling and Data Analysis for Engineers and Applied Scientists by Scott A. Pardo-P2P
English | 25 July 2016 | ISBN: 3319327674 | 264 Pages | PDF (True) | 9,70 MB


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  1. 工程师与应用科学家所需要的实用模型和数据分析 这本教科书会为工程类和应用科学类的高年级本科生以及低年级研究生讲授如何收集和分析实用发现数据,从而为设计决策做出帮助。科学是要发现问题的,然而工程和应用科学的主要方向是设计。在发现领域的科学家,一般来说希望理解自然世界而非改造它。而工程类和应用科学家们却相反地对问题设计产品、流程和解决方案。也就是说,统计学作为一门学科,更多地是偏向发现维度的。学过统计学相关课程的年轻工程师们,不一定会非常清晰地对能使用统计方法在他们的设计产品过程中起到多大作用有着什么作用。许多人只是认为统计学在演示设备或处理过程时真正地表现出其所设计的那个样子才是有用的。统计课程强调创建预测或分类模型——预测自然或将个体分类,统计学常被用来证明或反正现象,以帮助产品或流程的设计。然而在行业中,化学工程师使用设计好的实验来优化石油的提取;制造工程师使用实验数据优化机器运转;工业工程师会使用数据来优化在一个手工组装流程中操作员所需要的优化数据。这本书会为工程和应用科学学生们讲授如何将实用调查数据融入到这样的设计流程中。
    wilde(特殊组-翻译)10个月前 (07-23)