Transforming Data to Make Better Predictions
March 11, 2021 | 2:00 p.m. ET
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.