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Visual Six Sigma: Making Data Analysis Lean
book

Visual Six Sigma: Making Data Analysis Lean

by Ian Cox, Marie A. Gaudard, Philip J. Ramsey, Mia L. Stephens, Leo Wright
December 2009
Beginner to intermediate content levelBeginner to intermediate
504 pages
13h 37m
English
Wiley
Content preview from Visual Six Sigma: Making Data Analysis Lean

9.5. Modeling Relationships: Logistic Model

Recall that Mi-Ling intends to explore three modeling approaches: logistic regression, recursive partitioning, and neural nets. She considers using discriminant analysis, but recalls that discriminant analysis requires assumptions about the predictor variables—for each group, the predictors must have a multivariate normal distribution, and unless one uses quadratic discriminant analysis, the groups must have a common covariance structure. Thinking ahead to her test campaign data, Mi-Ling notes that some of her predictors will be nominal. Consequently, discriminant analysis will not be appropriate, whereas logistic regression will be a legitimate approach.

Mi-Ling begins her modeling efforts with a traditional logistic model. But before launching into a serious logistic modeling effort, Mi-Ling decides that she wants to see how a logistic model, based on only two predictors, might look. She begins by fitting a model to only two predictors that she chooses rather haphazardly.

Once she has visualized a logistic fit, she proceeds to fit a logistic model that considers all 30 of her variables as candidate predictors. She uses a stepwise regression procedure to reduce this model.

9.5.1. Visualization of a Two-Predictor Model

Since she will use only the training data in constructing her models, Mi-Ling copies the values in the row state variable Training Set to the row states in the data table CellClassification_2.jmp. As she did earlier, ...

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Publisher Resources

ISBN: 9780470506912Purchase book