Tom and John,
I am paying very close attention to everything you say. The
problem is that for a particular ratio in a particular industry (such
as supply expense per client per day), it is known and published by
the experts that the figure should be in the range of a few hundred
dollars. Depending on other factors (which are captured), this could
vary between $200 or so and maybe $700. Problem is, the incoming data
includes figures such as 22 cents and $125,000, both of which are
obviously incorrect. Alas, we have no control over the incoming data,
so the best we can do is to try to grab the "reasonable" figures that
are the bulk of the data and ignore the "unreasonable" figures that
have resulted from data input errors, misunderstanding of what is
being asked, etc.
What is wanted is Top, Second and Third Quartiles for each
indicator. These cannot be figured unless the ridiculous data is
excluded. Tom, I presume that when you say the AVERAGE function will
get me the average, this is the same as the mean, since neither
StdDevs nor quartiles can be figured without the mean, to my limited
mathematical knowledge.
I acually wasn't aware that there were a spreadsheet function for
quartiles, so that is interesting. I take it that this reveals true
quartiles, not just counting down 3 in a set of 12 data, for instance.
I am grateful to both of you for your interest in my problem. I
readily admit to being mathematically obtuse. I have hundreds of
indicators to run, so I'm looking for a fast and efficient way to
handle this. James
On Jul 9, 2:56?pm, wrote:
James,

I'm thinking like Tom....what is it exactly you are trying to get to?

Are you trying to eliminate outliers from your dataset?....all those
beyond 2.5 std devs. Or are you trying to find the 25th and 75th
quartiles?

John