Box Cox Power Transformation. This is useful for modeling issues related to heteroscedasticity non constant variance or other situations where normality is desired. There s a wonderful article by osborne.
There s a wonderful article by osborne. Specifically you can use the function boxcoxfit for finding the best parameter and then predict the transformed variables using the function bctransform. The box cox power transformation is not a guarantee for normality.
This is because it actually does not really check for normality.
Formally a box cox transformation is defined as a way to transform non normal dependent variables in our data to a normal shape through which we can run a lot more tests than we could have. When some of the data are negative a shift parameter c needs to be added to all observations in the formulae above x is replaced with x c. Currently power transform supports the box cox transform and the yeo johnson transform. A box cox power transformation refers to a way of transforming response to satisfy the usual regression assumption of homogeneity and normality of variance.
