1. Why do we have to normalise the data using transformations, won't it affect the relationship between variables? Although it seems to distort the scale of the data but it still reduce the effect of outliers?

2. I see that white noise for error should have 0 to very minimum co relation so that the error must be completely random and proving that the model is good but if the errors possess normality or stable variance then does it imply that error is not following any pattern and we didn't miss any variable to fit into our model?. Does residuals need to have normality to show it is a good model?

3. How is homeosedacity different from white noise?

4. How to calculate optimal lambda for box Cox transform?

5. Is there any simple derivation available for box Cox transformation?

Please if anyone can address all these questions...it will be very really helpful... thank you in advance.