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Collecting Good Data
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This section lays out some general principles for collecting data for
construction of process models. Using well-planned data collection
procedures is often the difference between successful and unsuccessful
experiments. In addition, well-designed experiments are often less
expensive than those that are less well thought-out, regardless of
overall success or failure.
Specifically, this section will answer the question:
What can the analyst do even prior to collecting the data
(that is, at the experimental design stage) that would allow
the analyst to do an optimal job of modeling the process?
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