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Definition
According to the International Statistical Engineering Association , Statistical Engineering is the study of the systematic integration of statistical concepts, methods, and tools, often with other relevant disciplines, to solve important problems sustainably.

Statistical Engineering - The Discipline
Statistical engineering is a discipline because it does not consist of a collection of tools, but is rather the art and science of solving complex problems that require data and data analysis. In this sense, it includes a theoretical aspect, of studying the underlying theory of how to best approach such problems, and an applied aspect of focusing on real problems. Problems requiring statistical engineering are typically large, unstructured, and cross several disciplines, although because of the data component, statistics is a key discipline.

Statistical engineering provides guidance to develop appropriate strategies to produce sustainable solutions to such complex problems. These strategies determine the statistical and analytic methods are appropriate to a given problem, depending on the circumstances, and outlines how to create solutions that are truly sustainable.

Figure 1 illustrates how statistical engineering integrates with the other two key aspects of the broader field of statistics; statistical thinking and statistical methods. In this view, statistical thinking represents the strategicaspect of the statistics field, that is, how to view the world from a stochastic versus deterministic point of view, understanding the role that empirical data analysis can play in scientific inquiry, and so on. The methods themselves represent the operationalaspect, that is, where the rubber hits the road on actual applications. Historically, however, some authors (e.g., Hoerl and Snee 2017 ) noted a gap between the strategic concepts, and the tools themselves. That is, it was not clear how the concepts actually guided use of the tools. Statistical engineering is intended to fill this gap by developing general tacticalapproaches based on the concepts of statistical thinking. These general approaches then call for, link, and integrate individual tools from statistics and other disciplines, effectively linking the strategy with operations. Each of the three elements, statistical thinking, statistical engineering, and statistical methods and tools, have theoretical underpinnings, and also a body of experience as to how they apply in practice, as illustrated in Figure 1.

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Core Processes
The core processes of statistical engineering are the key building blocks on individual applications. That is, they are a set of “what’s”, things to be done in a variety of ways, which integrated in some combination, form the core of statistical engineering applications. While the set of core processes is relatively constant, the way they are linked and integrated are unique to each application. The core processes are:


 * Data collection
 * Data exploration and visualization
 * Model building
 * Drawing inferences (learning) from models
 * Solution deployment and sustainability

International Statistical Engineering Association (ISEA)
ISEA was founded in 2018 to further develop and expound the discipline of statistical engineering. ISEA held the first annual summit on statistical engineering on October 1-2, in conjunction with the Fall Technical Conference. ISEA is a connected to several other professional societies, but is a stand-alone organization.

References:
International Statistical Engineering Association

Roger W. Hoerl & Ronald D. Snee (2017) Statistical Engineering: An Idea Whose Time Has Come?, The American Statistician, 71:3, 209-219, DOI: 10.1080/00031305.2016.1247015

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Also See:
JMP Analytically Speaking with Roger Hoerl

Christine M. Anderson-Cook & Lu Lu (2012) Editorial for Statistical Engineering Special Issue, Quality Engineering,24:2, 107-109, DOI: 10.1080/08982112.2012.654420