Transforming Data for Statistical Analysis: The Power of Box-Cox Transformation
Learn how to stabilize and normalize data using the Box-Cox transformation technique. This post provides a step-by-step guide and example code in Python’s scipy library for implementing the transformation.
Box-Cox transformation is a technique used to stabilize the variance of a variable and make it more normally distributed. It is commonly applied when dealing with data that violates the assumption of constant variance in linear regression or other statistical models.
The transformation involves applying a power transformation to the data, which can be adjusted to find the optimal transformation parameter lambda (λ).
The Box-Cox transformation is defined as follows:
y_transformed = (y^λ — 1) / λ if λ != 0
log(y) if λ = 0
where y is the original variable and y_transformed is the transformed variable.
The optimal value of lambda is typically determined by maximizing the log-likelihood function or minimizing another suitable criterion. However, in practice, different values of lambda are often tried to find a transformation that improves the data’s characteristics.
Here’s an example of how to perform a Box-Cox transformation using Python’s scipy library:
In this example, we generate skewed data with outliers by combining two normal distributions centered at 5 and 15, respectively, and add a few outliers with values 30, 35, and 40. We then plot the distribution of the original data using Seaborn’s histplot() function.
Next, we apply the Box-Cox transformation using stats.boxcox() from the scipy library. The transformed data and the estimated lambda value are stored in the variables transformed_data and lambda_, respectively.
