Box-Cox transform function belongs to the Power Transform family of functions. These functions are primarily used to create monotonic data transformations, but their main significance lies in the fact that they help in stabilizing variance by adhering closely to the normal distribution and making the data independent of the mean based on its distribution. This function has one prerequisite that the numeric values to be transformed must be positive (similar to what even log transform expects). In case they are negative, shifting using a constant value helps. Mathematically, the Box-Cox transform function can be defined as: Such that the resulted transformed output y is a function of input x and transformation parameter λ such that when λ= 0, the resultant transform is the natural log transform, which we discussed earlier. The optimal value of λ is usually determined using a maximum likelihood or log-likelihood estimation.