In article <00f301c1be68$13413000$fde9e3c8@oemcomputer>,
Voltolini <[EMAIL PROTECTED]> wrote:
>Hi,
>I would like to know if methods for detecting outliers
>using interquartil ranges are indicated for data with
>NON normal distribution.
>The software "Statistica" presents this method:
>data point value > UBV + o.c.*(UBV - LBV)
>data point value < LBV - o.c.*(UBV - LBV)
>where: UBV is the 75th percentile) and LBV is the 25th percentile). o.c. is
>the outlier coefficient.
>In the biological world many data are not normally distributed and tests
>like Rosner, Dixon and Grubbs (if I am wright ! ) are good just for normally
>distributed data.
Nothing is normally distributed; some may come close.
But are they even good for normally distributed data?
Why should anyone be concerned about outliers? If there
are observations produced under the assumed model, they
should be included, no matter how far out they are. The
only legitimate justification for excluding some data
points is that errors of some kind have occurred in
producing them, whether they are outliers or inliers.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
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