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It doesn't answer to my questions. But it's fair enough. Thank you anyway. Is there any french version of the ArcGIS pdfs ? Anybody can provide the exact french translation of :  Root Mean Square Error  Root Mean Square Standardized Error  Average Standard Error Regards
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09162020
08:03 AM

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745

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Dear Erik, I thank you deeply for your answers which I am sure are very clear. However, this is not exactly what could be of use to me. I do not intend to calculate the parameters by myself. But, until I did it for a little example, I couldn't figure it out. So if I am still allowed to abuse your time, I pose my problem in another way. But I'm sure your answer will help a lot of people in the community. From my experience in teaching, it is often the calculation of uncertainties that poses the most problems of understanding, and finally we find ourselves confronted during research work to see very nice models, but whose reliability and validity remains to be proven for lack of objective analysis of estimation errors. then, if you can explain us through the example of the pdf that you sent (page 54) which explains very well the principle of calculation of the kriging until the final estimate for an unsampled point by finally giving its variance and its standard error (page 58). until then I understood perfectly, well I think. Question 1 is it this standard error which is used to calculate the "average standard error" in "capture3.jpg"? is it the same in "Capture4.JPG"? if yes then it's ok. Let's now take 1 point and give it an estimate by crossvalidation: let's say sample (1,3) without doing the calculation let's say its new value is "104", with the error is 105104=1. Question 2: Could you calculate its standard error like in "Capture5.JPG"? Question 3 How is calculated the RMS error "Capture3.JPG"? is that from the whole prediction data or only from the cross validation data? Question 4 which standard error we use for standardizing the RMS "Capture3.JPG"? sorry to bother you and thank you again .. Cheers.
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09152020
10:33 AM

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745

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Thank you very much for the quick response. Yes if you can explain to me with a small example the procedure for calculating these different parameters, because this will be the only way for me to understand, not being familiar with mathematical formulas, and please specifying for each case if the data are those of the final interpolation or of the cross validation: 1 the standard error for an individual sample 2 the average standard error 3 the RMS error 4 the standard error we use for standardizing the RMS about the forgotten square in the 2009 formula, I have a corrected version. but a small difference in the title of the formula brought me a little clarification: in my version the word kriging has been removed from "average kriging standard error". what would be the exact expression? thanks again.
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09152020
08:25 AM

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745

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Hello ArcGIS community, I'm trying to understand how different errors of cross validation are calculated. I've the formula of desktop.arcgis.com page, but I could not find out for some of them. I'm using the ozone tutorial data. I've attached 2 files. Thank you to answer to these questions : 1 How is calculated the standard error for an individual sample ? from wich data ? I've tried unsuccessfuly different combinaison 2 How is calculated the average standard error ? I've found a different value for the average of the individual standard errors. But not for the stadardized error and the mean standardized, where I could recalculate standardized error from the standard error and the mean standardized from the mean of the individual standard errors. 3 How is calculated the RMS error ? is that from the whole prediction data or only from the cross validation data ? 4 which standard error we use for standardizing the RMS ? Is it possible, if you have time, to make a summary of how calculate all these parameters, it can be usefull for the whole community, as understanding the prediction errors is as important than prediction itself. Thank you so much for taking time to answer.
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09152020
02:11 AM

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