@kaune wrote: I get an intercept of -144.54 and a slope estimate of 3.91 (weight = 3.91*height - 144.54). The fixed effect estimates in the solution option (in PROC MIXED or PROC GLM) provides estimates for the reference subject only (which is the last one calculated), so simply re-ordering the data can vastly change the result. In this example, how do I determine the global effect of weight on height? The slope for height on weight for the various different clinics is indeed constant. The coefficients do change depending on which level is considered the reference. However, the sum of the global slope and effect due to the specific clinic is unique and doesn't change if you change the reference subject.   So to use Rick's example from SASUSER.CLASS data set:   Standard Effect clinic Estimate Error DF t Value Pr > |t| Intercept -135.67 42.9456 0 -3.16 . Height 3.7701 0.6818 13 5.53 <.0001 clinic 1 873.56 638.75 0 1.37 . clinic 2 -8.8650 77.9700 0 -0.11 . clinic 3 0 . . . . Height*clinic 1 -13.1818 9.7306 13 -1.35 0.1986 Height*clinic 2 0.1359 1.2906 13 0.11 0.9177 Height*clinic 3 0 . . . . the slope of height for clinic1 is 3.7701 - 13.1818 = -9.4117, and this is unique and unchanging, even when you change the reference level.   ... View more