Which criterion should be used for transformations of dependent variables? [duplicate]

When transforming the dependent variables, I know R^2 and related criterion is not suitable for model selection. Then which one should I use?

• the question is not clear... model selection for what?
– ABK
Mar 6 '20 at 10:49
• In a simple linear regression, I want to select the best lambda when doing box-cox transformation for the dependent variable . Thank you.
– Eva
Mar 6 '20 at 10:58
• See stats.stackexchange.com/questions/242526/… or the list. Probably a dup in there Mar 6 '20 at 13:14
• This question is so general that it deserves a general answer, which is provided by the duplicate.
– whuber
Mar 9 '20 at 14:31

Unless you are exclusively interested in prediction (and not at all in explanation) then I think you should only transform variables for substantive reasons, not statistical ones.

EDIT 2 For instance, it often makes sense to take logs of money variables such as income or price.

EDIT 1 Note that not all statisticians share my views.

If the assumptions of OLS regression are not met, then, rather than transform, you can use a different model, such as quantile regression or robust regression.

• Thank you for your suggestion. But I have to do some transformation for the response and compare them, which the requirement of my hw.
– Eva
Mar 9 '20 at 13:58
• It is worth noting that your opinion very well may be a minority one among statisticians, especially those exposed to Tukey's work on EDA. Tukey advocated using exploratory analysis to uncover effective ways of re-expressing data. This was one of the cornerstones of his approach to all data analysis.
– whuber
Mar 9 '20 at 14:33
• OK, I edited my answer. But Tukey didn't have the tools that we have - at least, not in any practical way. I know quantile regression is very old in theory, but it only became practical with computer. Mar 9 '20 at 18:59
• There are many situations e.g fitting power functions or exponentials where taking logs first is natural and convenient and often the best thing to do. I don't know whether you call that circumstance substantive or statistical. The same applies, although on the whole with less force, to several other transformations. Mar 9 '20 at 19:09
• With many kinds of data being willing to use logarithmic scale arises from past experience that it is a good idea. Some people call that theory. Mar 9 '20 at 19:20