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 several samples with extremely high values, which makes me
feel uncomfortable. The percentiles of the dataset are listed as follows
(in mg/kg):
Zn Cu Pb
Cd As
Min 4 1 25 0.0 2 5% 35 6 35 0.1 6 10% 40 7 41 0.2 7 25% 65 13 62 0.3 9 50% 122 18 168 0.6 15 75% 338 27 821 1.5 28 90% 907 56 2799 2.8 58 95% 1986 116 4490 4.2 80 96% 2462 151 4698 4.9 82 97% 3493 178 5413 6.2 91 98% 4697 207 7609 8.3 111 99% 6712 247 11750 12.4 184 Max 11473 1293 16305 48.5 1060 When doing geostatistical and statistical
analyses, we need some confidence in dealing with the these very high
extreme values which account for less than 2% of the total sample
number.
Any suggestions?
Cheers, Chaosheng Zhang
===================================
Dr. Chaosheng Zhang Department of Geography National University of Ireland Galway IRELAND Tel: +353-91-524411 ext. 2375 Fax: +353-91-525700 Email: [EMAIL PROTECTED] =================================== |
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