of course, if one has control over the data, checking the coding and making
sure it is correct is a good thing to do
if you do not have control over that, then there may be very little you can
do with it and in fact, you may be totally UNaware of an outlier problem
i see as a potentially MUCH larger problem when ONLY certain summary
statistics are shown without any basic tallies/graphs displayed so, IF
there are some really strange outlier values, it usually will go undetected ...
correlations are ONE good case in point ... have a look at the following
scatterplot ... height in inches and weight in pounds ... from the pulse
data set in minitab
- *
-
300+
-
Weight -
- 2
- 2 224 32
150+ * * 3458*454322*
- *53*3*535 2
- **
--+---------+---------+---------+---------+---------+----Height
32.0 40.0 48.0 56.0 64.0 72.0
now, the actual r between the X and Y is -.075 ... and of course, this
seems strange but, IF you had only seen this in a matrix of r values ...
you might say that perhaps there was serious range restriction that more or
less wiped out the r in this case ... but even the desc. stats might not
adequately tell you of this problem
IF you had the scatterplot, you probably would figure out REAL quick that
there is a PROBLEM with one of the data points ...
in fact, without that one weird data point, the r is about .8 ... which
makes a lot better sense when correlating heights and weights of college
students
At 09:06 PM 2/25/02 +0000, Art Kendall wrote:
>--------------6F47CB3D3B10A10A3E9B064C
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>An "outlier" is any value for a variable that is suspect given the
>measurement system, "common sense", other values for the variable in
>the data set, or the values a case has on other variables.
>=================================================================
Dennis Roberts, 208 Cedar Bldg., University Park PA 16802
<Emailto: [EMAIL PROTECTED]>
WWW: http://roberts.ed.psu.edu/users/droberts/drober~1.htm
AC 8148632401
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