Ok, we've talked it over, and we're going to change the graphic and some of the text from that topic. The 10.0 web help will update, and it will be changed in a future service pack as well as in version 10.1. Thanks for bringing this to our attention. Thank you. So in addition to not being Kriging model on the right, according to the parameters in the cross-validatin, the one on the right should be better than the left one. Is that correct?
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I have a question about the pic above. Below is the original descriptions attached to this pic. "For example, the root-mean-squared prediction error may be smaller for a particular model. Therefore, you might conclude that it is the optimal model. However, when comparing to another model, the root-mean-squared prediction error may be closer to the average estimated prediction standard error. This is a more valid model, because when you predict at a point without data, you have only the estimated standard errors to assess your uncertainty of that prediction. When the average estimated prediction standard errors are close to the root-mean-squared prediction errors from cross-validation, you can be confident that the prediction standard errors are appropriate. In the figure above, both kriging models are good, but those at the left are slightly better." My question is, that, the ones with the root-mean-squared prediction errors closer to average standard errors should be the right one, instead of the left one. Why does it say that those at the left are slightly better? Thanks.
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