How is data useful in tqm

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-Decision trees are popular due to their versatility (both categorical and numeric responses allowed), ease of interpretation, automatic construction through recursive partitioning, lack of distributional assumptions, and understandable visualization, although a large training data set is required. Each node represents a binary decision based on the input variables. How is this data useful in TQM? Can the data be used for SPC models as well?

-The best-pruned tree is the least complex tree within one standard error of the minimum error tree on the validation set. How can this be an asset using data acquired in production or in-service centers?

-Classification trees, Regression trees, What makes each of the trees unique? How are they applied in different aspects to assist businesses in their success?

Reference no: EM132956794

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