>From Moore and McCabe, Introduction to the Practice of Statistics (2003) p. 46
-------------- The interquartile range IQR is the distance between the first and third quartiles. IQR = Q3 - Q1 The 1.5 x IQR Criterion for Outliers Call an observation a suspected outlier if it falls more than 1.5 x IQR above the third quartile or below the first quartile. --------------- (You should informally investigate suspected outliers, looking for a reason to throw them out.) Kip Murray Sent from my iPad On Jan 9, 2012, at 6:49 PM, Roger Hui <[email protected]> wrote: > I wonder if there are well-known techniques in statistics for dealing with > the following problem. > > t > 11 10 10 10 10 11 10 10 10 10 9 11 10 11 10 10 11 10 11 10 11 10 10 > 11 10 11 10 10 10 11 10 74 11 11 14 11 11 10 12 11 15 14 12 11 > 11 11 11 11 10 12 11 11 11 10 11 11 11 10 11 11 10 11 161241 49 > 32 12 11 11 12 10 11 10 12 11 12 11 11 12 11 11 12 11 11 11 12 > 11 11 12 11 11 11 11 11 11 11 10 11 11 12 12 > > t is a set of samples from a noisy source which is supposed to give the > same integer answer. Obviously, 161241 is an "outlier", and it is likely > that 74, 49, or even 32 are outliers too. Are there standard techniques > for discarding outliers to clean up the data, before the application of > statistical tests such as the means test or large sample test? > ---------------------------------------------------------------------- > For information about J forums see http://www.jsoftware.com/forums.htm ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
