# Forecasting after Box-Cox transformation

I did a regression and found that my residuals pointed out that my data is heteroscedastic. I applied the Box-Cox transformation and my new model looks as follows,

y^(1/5) = b0 + b1x1 + b2x2


with my lambda = 1/5 and I found my b0, b1 and b2 values. This transformed model does not seem to have heteroscedasticity but now I want to use these coefficients to forecast data on a hold out set I have and test the accuracy. But is it correct if i use the coefficients from the transformed model and the resulting value I must get the power of 5 in order to get the actual value forecasted that I want to compare with the actual?

• Yes, that's correct. It's not clear why you are puzzled on this, but transformations must be inverted to get back to the original scale. Apr 22, 2014 at 8:43