Dear all I am having difficulty understanding why none of you want to try a spatial approach to statistics. Everyone is trying to make the 'independent' statistical tests work on spatial data. Try turning this around and look at the spatial aspect first.
(1) Testing variances: the sill on the semi-variogram (total height of model) is theoretically a good estimate for the sample variance when auto-correlation or spatial dependence is present. Do your F test on that. Yes, you still have degrees of freedom problems, but with thousands of samples the 'infinity column' should be sufficient. (2) Testing means: the classic t-test in the presence of 'equal variances' requires the 'standard error' of each mean. For independent samples, this is s/sqrt(n). For spatially dependent samples, this is the kriging standard error for the global mean. Your only problem then is getting a global standard error. Isobel http://geoecosse.bizland.com/whatsnew.htm
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