Transforming Data to Make Better Predictions
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See how to:
- Understand why transformations stabilize variance, make the error more uniform across the design region, remedy lack of fit and plot predictions in a way that does not violate physical limits, display negative counts or erroneously report yields as greater than 100%donn
- Transform data on the fly using Graph Builder and change scales to improve graph readability and interpretability
- Use square root transformation to eliminate negative values and examine how using Box-Cox power transformation on response might change fit
- Compare no transformation, log transformation with prediction in raw units and log transformation with prediction in log units
- Use square root transformation to construct model effects using Polynomial to Degree 2 macro, identify best transformation, use selected transformation, save residuals to data table and evaluate results using distributions
- See how square root transformation might display a more uniform count spread and more linear relationship
