Intermediate

Transforming Data to Make Better Predictions

Application Area:
Statistics, Predictive Modeling and Data Mining

Learn to understand the value and pitfalls of transforming data, and how to choose an appropriate transformation that will yield a logical and useful model. Understand how to handle one of the statistical assumptions for regression - that the error (variance) is distributed normally and uniformly across the range of the data. See how to transform data to a new scale to make the error better match this criterion to avoid possibly creating a model yielding physically impossible and potentially embarrassing results, such as negative values of hardness, resistivity, or the number of defects.

This webinar covers: an overview of principles of data transformation, descriptions of situations where transformation is important, and several case studies using Fit Model and Box-Cox transformations.

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