Cross Validation/calculate StdError, Stdd_Error, NormValue

[MarcGoetzke]

I think there is a really simple answer. I got a Table after using the Cross Validation Tool in ArcGIS. I dont know how you get the StdError, Stdd_Error and the NormValue. The StdError is the Standart Error of Prediction so i think its just SE = standard deviation / √n. I googled a lot and i have no idea. Can anybody help. Thx

FID	Measured	Predicted	Error	StdError	Stdd_Error	NormValue
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20	470,073195 912,403881 453,583461 483,765499 541,13589 766,325671 288,17376 904,216706 659,680241 676,788004 666,22003 480,821565 287,342394 123,791379 938,267365 938,267365 646,036572 443,277522 1087,176937 827,379415 778,769581	779,4643553 482,4430333 631,5594783 439,5693293 574,8925245 885,4098264 508,1779974 719,7978101 689,80433 679,3637665 637,4872852 413,8124286 577,9679624 658,5721968 936,7664019 919,780191 354,8780993 675,5704577 731,8108183 949,9845209 852,6773521	309,3911603 -429,9608477 177,9760173 -44,19616975 33,75663448 119,0841554 220,0042374 -184,4188959 30,12408898 2,575762471 -28,73274476 -67,00913635 290,6255684 534,7808178 -1,500963141 -18,48717397 -291,1584727 232,2929357 -355,3661187 122,6051059 73,90777111	197,9004352 187,2076829 222,1929554 222,8177887 225,6868838 217,4111495 204,8373173 157,9156231 170,0014572 192,6182741 189,5340294 211,093082 268,3646909 184,6837889 65,63124213 66,37976517 206,0541435 225,8855784 198,56918 194,0884513 205,7042675	1,563367761 -2,296705141 0,80099757 -0,198351173 0,149572868 0,547737113 1,074043735 -1,16783186 0,177199005 0,013372368 -0,15159676 -0,317438808 1,082950098 2,895656522 -0,022869644 -0,278506167 -1,413019257 1,0283655 -1,789633813 0,63169707 0,359291385	1,465233798 -1,98075241 0,637484163 -0,497200583 0 0,366106371 0,967421551 -0,967421551 0,119648099 -0,119648099 -0,366106371 -0,791638598 1,179761108 1,98075241 -0,241040395 -0,637484163 -1,179761108 0,791638598 -1,465233798 0,497200583 0,241040395

[EricKrause]

StdError is the standard error of prediction.  This is equal to the square root of the kriging variance, which is one of the outputs of the kriging equations.

Stdd_Error is the standardized error.  This is equal to Error divided by StdError.  Ideally, these values should follow a standard normal distribution.

NormValue is the equivalent quantile on the standard normal distribution for each standardized error.  If you make a scatterplot of NormValue versus Stdd_Error, this is called Normal QQPlot, and it is useful for testing whether the standardized errors actually do follow a standard normal distribution.  Ideally, the points should create a straight line at a 45 degree angle in the scatterplot.

[MarcGoetzke]

Thx Eric Krause. Helped me a lot;)