> If two plots next to each other are greatly different, > compared to the consistency of the 6 measures > taken on each plot, that result seems to demonstrate > that something is effective about the treatment that > differentiates the plots.
Plots are in forested area. On each plot 6 trees had its level of defoliation assesed. The 'treatments' are different soil conditions and tree's age. The number of trees was selected for economical reasons. I suspect that more trees on plot e.g. 20 could make smaller differences in averagaes between plots. This is where the idea of creating clusters of 3 or 4 plots and treating it as one one plot with 18 - 24 trees comes from. > If the two plots had the same treatment and yet differ that > way, then it has to indicate that the locations make a > difference, and the interaction has to serve as the error > term for testing. I agree. My assumption is (it comes from analogy to other forest research) that when you have more trees on plot the differences between trees on one plot caused by the factors which we cannot maeasure are reduced and make more visible differences caused by more general natural conditions on plots which we can measure. > From the little you have implied about the purpose, > I don't see any justification for averaging or smoothing. I hope that above I wrote more about my problem. The purpose is that I would like to use the point data for interpolation e.g. kriging > i also don't see the meaning of 'resampling' in the subject line. Perheps it is my misuse of the term. By resampling I meant creating clusters of 3 or 4 neighboring plots and treating them as one plot with coordinates of the center of the cluster Thanks for help Regards Rafal . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
