Dear Farhan Khan,

You can use the 'Cross-validation method' for all the data to estimate the trend and autocorrelation models. It removes each data location one at a time and predicts the associated data value. For example, a diagram having 10 data points. Cross-validation omits a point (normally red point) and calculates the value at this location using the remaining 9 points (normally blue points). The predicted and actual values at the location of the omitted point are compared. This procedure is repeated for a second point, and so on.

Thus, for all points, cross-validation compares the measured and predicted values. In a sense, cross-validation cheats a little by using all the data to estimate the trend and autocorrelation models. After completing cross-validation, some data locations may be set aside as unusual if they contain large errors, requiring the trend and autocorrelation models to be refit.

Or if you using Kriging method for interpolation there you have used QQ plot also based on normal data vs standard error data.

For your better understanding I have attached an article, please check it.

https://pubs.acs.org/doi/abs/10.1021/ci025626i?casa_token=JazbC9ejlEcAAAAA:aqCCNRIWDWnqMMXrAAXMsDae5Ir6wtBqJ2so0LjqThGlp31thVTHa1fekJRlskODgPsQYwArCLh2UJyP

Best wishes with regards,

Suraj

Suraj Kumar Mallick thankyou for ur response

Dear Farhan,

also take a look at this

http://help.arcgis.com/es/arcgisdesktop/10.0/pdf/tutorial_geostatistical_analyst.pdf