Dear all,
My question is: How to deal with the
extreme/outlying values in a data set?
I am dealing with heavy metal
concentrations in soils from a mine area. The sample number is 223, and the
samples are spatially evenly distributed with the sampling interval of 400
metres. There are sev
> My question is: How to deal with the
> extreme/outlying values in a data set?
The real priority is to establish why you have extreme
highs. For example:
(1) is there a high imprecision in measuring the
values, so that the sample observations are actually
inaccurate? If so, is it relative to the
Dear Isobel,
Thanks for your quick and helpful reply!
(1) I would like to trust both the accuracy and precision of the dataset,
and the real problem is how we "play the computer game". The extreme values
may be from the samples which by chance contains many minerals.
(2) From the information of
Hi, we have multiyear point data (gridded) of a measured variable, Y,
from farm fields and there are spatial patterns to Y. We want to
measure the degree of similarity of these patterns from year to year.
Could someone please point me in a direction or provide references?
thanks, chris
--
* To
Chris
There are two ways of approaching data which has a
time element:
(1) treat time as a co-variable and use co-kriging.
You would probably want to do this if you have more
than one variable anyway
(2) treat time as a dimension -- as an additional
co-ordinate. If your original data is two-dim
Dear Chaosheng Zang
The sampling interval is so wide that the high values could easily be related to "hot
spots" of
higher grade contamination, i..e dumping areas for particular kinds of slags,
mineralized
waste, etc. A property map might help.
Have you contoured the data? If so, the samp
