Hi, I have a problem that is giving me a headache. I have measured two angles that I believe to be related to one another, and they are (this is a data set where I have measured the angle from a datum to two features on a bone. There are 14 bones in the data set): Angles to feature 1 (F1): 15.225 14.2318 9.4301 12.2947 14.8846 7.6533 9.0948 11.9725 4.2773 14.1819 8.841 17.1037 20.2373 13.4599 Angles to feature 2 (F2): 3.1227 9.4799 7.9047 13.4962 8.5454 24.2871 11.443 12.6693 21.5271 4.0733 5.0085 4.0101 5.4445 16.424 When I plot F1 vs F2 and do a linear correlation I get an r^2 = 0.47. Related, but not very strongly. The thing is, I'm doing this because I have a bunch partial bone specimens and cannot define the datum, so in general I'm going to have the angle between feature 1 and feature 2, and am hoping to be able to get the position of the datum from this angle. So if I plot (F1-F2) vs F1 I get a much better correlation (r^2 = 0.74) and (F1-F2) vs F2 gets even better (r^2 = 0.9)! What I don't understand is, how can a linear combination of the two be better than either one alone? I have added no information and the equations are not linearly independent. What am looking at in the plots with the difference? I have attached plots of F1 vs F2, Diff vs F1 and Diff vs F2 with the regressions plotted. Thanks for your help.