Hello all, I have a question: I am using the interquantile method to spot outliers & it gives me values of say 234 & -120 or for the higher & lower benchmarks. I don't have any issues w/ the higher end. However I don't have any negative values. My lowest possible value is 0. Should I consider 0 as an outlier? Thanks ahead for your thoughts -- View this message in context: http://r.789695.n4.nabble.com/Stat-question-How-to-deal-w-negative-outliers-tp4664068.html Sent from the R help mailing list archive at Nabble.com.
Hi, This is not an R question and is therefore not appropriate for this list. You should post to a statistics forum such as http://stats.stackexchange.com/. But the answer is NO! Best, Ista On Fri, Apr 12, 2013 at 9:49 AM, ramoss <ramine.mossadegh at finra.org> wrote:> Hello all, > > I have a question: I am using the interquantile method to spot outliers & > it gives me values of say 234 & -120 or for the higher & lower benchmarks. > I don't have any issues w/ the higher end. However I don't have any > negative values. My lowest possible value is 0. Should I consider 0 as an > outlier? > > Thanks ahead for your thoughts > > > > -- > View this message in context: http://r.789695.n4.nabble.com/Stat-question-How-to-deal-w-negative-outliers-tp4664068.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
> -----Original Message----- > I have a question: I am using the interquantile method to > spot outliers & it gives me values of say 234 & -120 or for > the higher & lower benchmarks. > I don't have any issues w/ the higher end. However I don't > have any negative values. My lowest possible value is 0. > Should I consider 0 as an outlier?If your limits have been appropriately set and the lower outlier bound is -120, obviously not. However, if your data are constrained positive and unusually small values are as likely to be erroneous as unusually large values, you probably should have set the bounds differently. Try taking logs and see if that gives you a more or less symmetric distribution? S Ellison ******************************************************************* This email and any attachments are confidential. Any use...{{dropped:8}}