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Since engineering data are subject to variations in physical characteristics, actual data do not always perfectly match the predicted values. In an ideal situation we could state that the data correlate over a bounded range IF all of the values of the data set are perfectly aligned on the mean trend line as illustrated in Panels A and B of Figure 48.6. In reality, data are typically dispersed along the trend line representing the mean values. Thus, we refer to the convergence or data variance toward the mean as the degree of correlation. As data sets regress toward a central mean, the data variance or correlation increases toward r = +1 or -1 as illustrated in Panels D and E. Data variances that decrease toward r = 0 indicate decreasing convergence or low correlation. Therefore, we characterize the relationship between data parameters as positive or negative data variance convergence or correlation.

Rewind to 1998 eons ago in Internet time. Several developers are using the cf-talk e-mail list hosted by HouseOfFusion.com to discuss common problems in developing Web applications. The group includes Steve Nelson, Robi Sen, Joshua Cyr, Michael Dinowitz, and Gabe Roffman. As is the case with all such discussions, it begins with, Wouldn t it be nice . . . The group actively encourages freewheeling thinking. New people with new ideas are invited to participate. What emerges is an idea for an architectural framework to deal with some of these common problems. The group s members, realizing that something concrete has come out of their late-night discussions, casts about for a name for this new framework. Because we re all working in ColdFusion, says Steve Nelson, why don t we call it something like Fusebox

C++ Primer, Fourth Edition By Stanley B Lippman,Jos e Lajoie,

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Our discussions of statistical in uences on system design practices were predicated on a basic understanding of statistical methods and provided a high-level overview of key statistical concepts that in uence SE design decisions. We highlighted the importance of using statistical methods to de ne acceptable or desirable design ranges for input and output data. We also addressed the importance of establishing boundary conditions for NORMAL operating ranges, CAUTIONARY ranges, WARNING ranges, as well as establishing safety margins. Using the basic concepts as a foundation, we addressed the concept of cumulative errors, circular error probabilities (CEP), and data correlation. We also addressed the need to bound acceptable or desirable system outputs that include products, by-products, and services. Statistical data variances have signi cant in uence on SE technical decisions such as system performance, budgets, and safety margins and operational and system effectiveness. What is important is that SEs: 1. Learn to recognize and appreciate engineering input/output data variances 2. Know WHEN and HOW to apply statistical methods to understand SYSTEM interactions with its OPERATING ENVIRONMENT.

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In the normal course of things, Fusebox would have been an interesting idea laid aside and forgotten in the crush of daily business, but such was not the case this time Other developers, frustrated by a set of problems repeatedly cropping up in each new project, heard about Fusebox and began adopting it As ideas began to emerge and Fusebox began to evolve, it became less clear what someone meant when they said Fusebox Which of the many ideas that together constituted Fusebox were they talking about That both an early Fusebox and a later Fusebox were developed seems clear now Fusebox 1 was the name given to the original version of Fusebox In 2000, Steve Nelson and Craig Girard published Fusebox: Methodology and Techniques, documenting the new ideas that had been adopted by the burgeoning Fusebox community.

Pub Date: February 14, 2005 Print ISBN: 0-201-72148-1 Pages: 912

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