Boxcox Plot. So logically speaking the horse which came first and the fast horses along with it will have the same difference of completion time whereas the slowest ones will have a larger difference in their completion time. Imagine you are watching a horse race and like any other race there are fast runners and slow runners.
The basic idea behind this method is to find some value for λ such that the transformed data is as close to normally distributed as possible using the following formula. The box cox normality plot shows that the maximum value of the correlation coefficient is at lambda 0 3. The x axis of the plot represents λ values for a transformation of the data.
The x axis of the plot represents λ values for a transformation of the data.
The box cox linearity plot is a plot of the correlation between y and the transformed x for given values of lambda. Most data transformations can be described by the power function as a reminder here are the commonly used transformations. Box cox transformation using python. The histogram of the data after applying the box cox transformation with lambda 0 3 shows a data set for which the normality assumption is reasonable.
