Quality Progress - June 2015 - 52

STATISTICS ROUNDTABLE BY ROGER W. HOERL AND RONALD D. SNEE Guiding Beacon Using statistical engineering principles for problem solving QUALITY PROFESSIONALS are often to generate improved results."1 Applica- involved and political considerations at faced with solving major organizational tions of this discipline produce improved play. Too often, data analysis begins with problems such as: "Customers are com- results because statistical engineering is the data. This can be seen in various data plaining about the quality of our product grounded on sound underlying principles analysis competitions, such as those on and returning it"; "Our major process is that address the critical elements of ef- kaggle.com. Keep in mind, however, that producing an unacceptable amount of fective problem solving. In short, the key the data are not the problem; the problem defective product"; or "The regulatory elements of the principles of statistical is the problem. That is, we should view agency has identified a major environmen- engineering are (see Figure 1): data as a "how," and the original problem tal problem associated with one of our * Proper understanding of the problem trying to be solved as the "what." operations." How should quality professionals approach such problems, which are clearly not textbook with one correct answer? Where should they begin the problemsolving effort? What should be considered? How can the projects be set up for success? The fundamentals of statistical context. * A well-defined strategy for problem solution. * Evaluation of the pedigree of the associated data and information. * Integration of sound subject matter knowledge with data analysis. * Sequential approaches involving the Once we are clear on the problem we are trying to solve and its context, we can determine the type and amount of data needed to solve it. Conversely, if we already have data, clarification of the problem helps determine how the data can be best used to solve the problem. "Data have no meaning in themselves; they are engineering can provide valuable guidance testing of existing hypotheses and meaningful only in relation to a conceptual for these types of complex problems. development of new hypotheses. model of the phenomenon studied,"2 wrote Statistical engineering has been defined George Box, Bill Hunter and Stu Hunter. as: "The study of how to best utilize Understanding problem context statistical concepts, methods and tools, Problem context is everything we know business solution is not always the best and integrate them with information about the problem, including its history, statistical solution. For example, we may technology and other relevant sciences what has been tried before, the technology determine from initial analysis of exist- In addition, the best technical or ing data that they are not appropriate or sufficient for solving the problem at hand; additional, better quality data are needed. Performing sophisticated or detailed analysis of the current data would simply waste time at this point. In other cases, a simple analysis is all that is needed because the answer is obvious from basic graphs. The bottom line is that the context of the problem, not statistical metrics, determines the best business solution and the level of sophistication needed. Well-defined strategy Some practitioners have a favorite tool, whether it is multiple regression, time-series analysis or a nonparametric method. 52 QP * www.qualityprogress.com http://www.kaggle.com http://www.qualityprogress.com

Table of Contents for the Digital Edition of Quality Progress - June 2015

Up Front
LogOn
Expert Answers
Perspectives
Keeping Current
Mr. Pareto Head
What’s Your Next Move?
Like Abilities
Assessing the Landscape
Change in Flow
Quality in the First Person
Career Corner
Statistics Roundtable
Standards Outlook
Special Section
QP Toolbox
QP Reviews
One Good Idea
Back to Basics

Quality Progress - June 2015

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