Denis Cote writes,

I am still struggling with standardized coefficients you suggest in your regression book.

First, I was surprised you don’t seem to mention at all completely standardized coefficients (betas). There are so ubiquitous. Is there any other reason for not standardizing Y other than to keep its original scale? What about meaningless score in some tests?

Second, which coefficients would be best to graph? Are the standard errors from the regression with z2 Xs meaningful and comparable? What would be the appropriate error bar to graph?

…

By the way, I am turning my tables into graphs!

My reply: I’m not sure what you mean by “completely standardized coefficients.” We do suggest standardizing continuous input variables by subtracting the mean and dividing by 2 standard deviations. This seems like completely standardizing to me. Also, yes, standardizing y can make sense also. Although in practice we often standardize by taking logs. And, yes, I do think that, typically, coefs for different predictors can be graphed and compared–if the inputs are binary or else have been rescaled. (I like to center binary inputs but it does not typically make sense to rescale a binary input, since a change of 1 unit is already interpretable.)