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Visual Six Sigma: Making Data Analysis Lean by Leo Wright, Mia L. Stephens, Philip J. Ramsey, Marie A. Gaudard, Ian Cox

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3.5. Visual Six Sigma Data Analysis Process and Roadmap

As this quick tour of functionality may suggest, JMP has a very diverse set of features for both EDA and CDA. The earlier section "Visual Displays and Analyses Featured in the Case Studies" gave a preview of those parts of JMP that you will see again in later chapters. But, there is a danger that the section's techniques may have appeared as a laundry list, perhaps at odds with our contention in Chapter 2 that Visual Six Sigma is a way of combining such techniques to get value from data. Let's attempt to put the list of techniques into some context.

In Chapter 2, we discussed the outcome of interest to us, represented by Y, and the causes, or inputs that affect Y, represented by Xs. As we saw, Six Sigma practitioners often refer to the critical inputs, resources, or controls that determine Y as Hot Xs. Although many Xs have the potential to affect an outcome, Y, the data may show that only certain of these Xs actually have an impact on the variation in Y. In our credit card example from Chapter 2, whether a person is an only child or not may have practically no impact on whether that person responds to a credit card offer. In other words, the number of siblings is not a Hot X. However, an individual's income level may well be a Hot X.

Consider the Visual Six Sigma Data Analysis Process, illustrated in Exhibit 3.29, which was first presented in Chapter 2. In your Six Sigma projects, first you determine the Y or Ys of interest ...

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