- Thread starter Greybeard
- Start date
- Tags log-transform normality issue

Using a Log10 transformation on the DV, it results in a skewness of .507 with a std error of .075 well above the target +/- 1.96 range.

Note that if you are using some forms of maximum liklihood as an estimator (compared to OLS) this will commonly require normality to work even if regression itself does not. But most likely with linear regression you won't be using ML anyhow.

If you are doing blocks than what you likely care about is the signficance of the F change test which determines if the R squared increased signficantly. I don't know if normality is required for the F test, although I doubt it.

This is one of those areas where what classes commonly teach and statisticians generally believe appears to be very different. There is often tremendous focus in classes on statistics on normality, even though its not actually a part of the central Gaus-Markov assumptions of regression. That has been really hard for me to accept - given how often I was taught the importance of normality.