This really ought to be better known. I think a large part of the reason that it isn't is because most books that cover linear models in general require a background in linear algebra, and there are very few people teaching from that standpoint outside of the advanced undergraduate/beginning graduate level.

Another thing that I wish was more widely known is that a linear model is linear in its parameters, not the data. You can apply arbitrary transformations to the data and still have a linear model as long as what you're fitting is of the form Ey = \beta_0 + \beta_1 f_1(X) + \beta_2 f_2(X) + ... + \beta_p f_p(X).