Hi Digby, Just a note - in circumstances that you have just described, the greater the level and range of autocorrelation means the more precise your estimate of the mean will be.
If your 1000 cores were randomly sampled from the population of 1 million, then the fact that some (perhaps many) of pairs of datapoints lie less than the (variogram) range apart will not matter. s^2 is a valid, unbiased estimate of the population variance. (The population is defined here as being the 1,000,000 possible cores that could be taken from this area - not of the process that generated this realization/data). What's more the typical simple random sample (SRS) standard error (s^2/n), will perform exactly as expected. If you chose to use a more sensible design, say a grid (systematic sample) .. then your s^2/n would be in fact be an _overestimate_ of the standard error. Mat -----Original Message----- From: Digby Millikan [mailto:[EMAIL PROTECTED] Sent: Thursday, 9 December 2004 8:32 a.m. To: ai-geostats Subject: Re: [ai-geostats] Re: Sill versus least-squares classical variance estimate RE: [ai-geostats] Re: Sill versus least-squares classical variance estimateColin, You misunderstood me, the 1 million data is the total unknown dataset. Say you have a volume in a mine and it's volume is 1 million 1 metre core samples. You drill the volume and have a sample set of 1000 1m core samples. You then analyse the statistics of the 1000 samples to try and estimate the variance of the total volume (1 million core samples). So your estimate of the variance comes from the 1000 samples. You can plot the variogram of the 1000 samples and you can also calculate it's variance. You are trying to estimate the variance of the 1 million peices of core which you do not have. So you must decide wether your 1000 sample set is a true representation of the 1 million. Our argument is that samples within the 1000 which are clustered together do not create a good representation of the true dataset and will create a biased estimate. Digby www.users.on.net/~digbym
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