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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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2.1. Background: Models, Data, and Variation

There is no doubt that science and technology have transformed the lives of many and will continue to do so. Like many fields of human endeavor, science proceeds by building pictures, or models, of what we think is happening. These models can provide a framework in which we attempt to influence or control inputs so as to provide better outputs. Unlike the models used in some other areas, the models used in science are usually constructed using data that arise from measurements made in the real world.

At the heart of the scientific approach is the explicit recognition that we may be wrong in our current world view. Saying this differently, we recognize that our models will always be imperfect, but by confronting them with data, we can strive to make them better and more useful. Echoing the words of George Box, one of the pioneers of industrial statistics, we can say, "Essentially, all models are wrong, but some are useful."[]

2.1.1. Models

The models of interest in this book can be conceptualized as shown in Exhibit 2.1. This picture demands a few words of explanation:

  • In this book, and generally in Six Sigma, the outcomes of interest to us are denoted by Ys. For example, Y1 in Exhibit 2.1 could represent the event that someone will apply for a new credit card after receiving an offer from a credit card company.

  • Causes that may influence a Y will be shown as Xs. To continue the example, X1 may denote the age of the person receiving the ...

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