On 16 Jan 2002 11:33:15 -0800, [EMAIL PROTECTED] (Wuzzy) wrote: > If your beta coefficients are on different scales: like > you want to know whether temperature or pressure are affecting > your bread baking more, > > Is the way to do this using Beta coefficients calculated > as Beta=beta*SDx/SDy
Something like that ... is called the standardized beta, and every OLS regression program gives them. ... > > It seems like the Beta coefficients are rarely cited in studies > and it seems to me worthless to know beta (small "b") as you are > not allowed to compare them as they are on different scales. In biostatistical studies, either version of beta is pretty worthless. Generally speaking. What you have is prediction that is barely better than chance. The p-values tell you which is "more powerful" within this one equation. The zero-level correlation tells you how they related, alone. -- If these two indicators are not similar, then you have something complicated going on, with confounding taking place, or joint-prediction, and no single number will show it all. - When prediction is enough better-than-chance to be really interesting, then the raw units are probably interesting, too. [ ... ] > Is there a way of converting this standardized coefficient to a > "correlation coefficient" on a scale of -1 to +1) > It would be useful to do this as you want to know the correlation > coefficient of temperature after factoring out pressure. I think you are looking for simple answers that can't exist, even though there *is* a partial-r, and the beta in regression *is* a partial-beta. The main use I have found for the standardized (partial) beta is the simple check against confounding, etc. If ' beta' is similar to the zero-order r, for all variables, then there must be pretty good independence among the predictors, and interpretation doesn't hide any big surprises. If it is half-size, I look for shared prediction. If it is in the wrong direction or far too big (these conditions happen at the same time, for pairs of variables), then gross confounding exists. Hope this helps. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =================================================================
