[sushiboat]

The coefficients are derived from the data, so you can't just change the
positive/negative sign without hurting the equation's performance. I could
take out all but one of a set of highly correlated predictors. However, all
predictors in the equation are making a highly significant contribution.
Perhaps a better solution would be to create a composite variable. A new
variable called Bigness could be the sum of the standardized scores of Size,
HP, and Strength. A little information would be lost by not keeping them
separate, but not much.

Edit: EvilDave, I don't have the output in front of me. (It's a Mac Classic app,
and I don't want to fire up the Classic environment just now.) I do remember
the predictors I kept having p-values that were very low, less than .0001. I
removed the predictors with p-values of .10 and higher from the three most
recent regressions. It just happened that there was a huge gap between the
predictors that weren't statistically significant and the ones that were.