4 months ago
#3511 Quote
The easiest way to interpret your data is to go back to kindergarten and play the “which one of these is not like the others” game.  That assumes most if your data is coming from “normal and healthy” locations and only one or two are “outliers”.  So if you see 10 sensors reading 50% RH and one reading 90% RH you might want to wonder why.  Make sense?  Its a lot harder to create an enumerated list of “under these conditions expect to see XX % WME” as there are many variables that go into deciding what is “normal”.

Put another way, use all of your data to create a baseline for normal and then look for outliers.
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