Not to oversimplify ... 1. (At least) dozens of books and thousands of papers have been written on this...
2. Most important question is: What is an outlier? (Many smart folks says that the concept is illogical/flawed -- there is no mystical boundary that one crosses to become a statistical pariah; many other smart folks disagree). 3. Equivalently: What is the model with respect to which values are outlying? (with apologies to Winston Churchill's: "That is an indignity up with which I will not put.") So good advice here is: Beware of good advice about this. (Of course, I may just be an outlier ...) ;) Cheers, -- Bert Gunter Genentech Non-Clinical Statistics South San Francisco, CA "The business of the statistician is to catalyze the scientific learning process." - George E. P. Box > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of > [EMAIL PROTECTED] > Sent: Thursday, September 23, 2004 7:22 AM > To: [EMAIL PROTECTED] > Subject: [R] detection of outliers > > Hi, > this is both a statistical and a R question... > what would the best way / test to detect an outlier value > among a series of 10 to 30 values ? for instance if we have > the following dataset: 10,11,12,15,20,22,25,30,500 I d like > to have a way to identify the last data as an outlier (only > one direction). One way would be to calculate abs(mean - > median) and if elevated (to what extent ?) delete the extreme > data then redo.. but is it valid to do so with so few data ? > is the (trimmed mean - mean) more efficient ? if so, what > would be the maximal tolerable value to use as a threshold ? > (I guess it will be experiment dependent...) tests for > skweness will probably required a larger dataset ? > any suggestions are very welcome ! > thanks for your help > Philippe Guardiola, MD > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html >
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