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Mike Burkes wrote (reassembled)-

>  I noticed a number of members averaged
> the entire moon set but upon my graphing the set it becomes readily
> apparent sites 21-01-40 and 21-03-22 are rejected therefore the line
> of best fit falls nicely thru the remaining 6 sites and solving no 3,
> the 21-00-48 site, yields an agreeable solution  and a run-on sentence

=======================

Response from George.

I've made Jeremy's 8 Moon observations, being discussed here,  available as
an Excel attachment to illustrate the points involved.

Mike Burkes has his own way of doing these things, but I suggest that the
procedure he describes should not be copied. It may be of interest to the
list to start a bit of discussion about methods of handling scatter, to
discover the attitude of others, and Mike's message provides a good starting
example. I will present my own views, which I like to think of as
"scientific", to provoke some comment. I will go into this particular
sight-reduction in some detail, not because it's an important matter in
itself, but because it provides such an example of different approaches. I
hope Mike isn't too sensitive to being used as an example in this way, and
hope he'll argue back if he thinks fit.

Jeremy has explained that the Moon limb was unsharp because the contrast
with the bright sky background was very low, and therefore he was not
confident about the precision of those lunar altitudes. Presumably, that
uncertainty applied equally to each of the 8 observations, which show an
unexpected amount of scatter.

Mike wrote "but upon my graphing the set it becomes readily apparent sites
21-01-40 and 21-03-22 are rejected ...". Rejected by Mike, but on what
grounds? In my view, the only valid reason for rejecting some sight from a
set, and accepting others, is if it is so far out of line that it must have
been the result of a blunder. If, on the other hand, it's the result of
scatter due to difficult observing conditions, then one such observation is
as good as any other, and the way to deal with the scatter is simply to
average the lot, giving equal weight to every point. Because outliers are
fewer in numbers than the main group, their contribution to the averaged
result is correspondingly limited.

However, if I were in a generous mood, I might be persuaded to go along with
Mike's rejection of the very highest observation, made at 21:01:40, as being
particularly far out of line with the rest, and perhaps plausibly the result
of some sort of blunder; in which case I might reluctantly accept leaving
that point out and averaging the rest.  But what justification can he then
offer for rejecting also the next-highest, at 23:01:22, while accepting the
lowest, at 21:05:08? One is no further out-of-line with the rest of the
group than is the other.

But in the end Mike rejects much more than than. He looks at the six points
that remain after discarding the highest two, and decides that on some basis
he can draw a line through them, a line which happens to pass through the
sight taken at 21:00:48. So he then discards everything else and works on
the basis of that one point only. This process may have saved him a bit of
arithmetic, in avoiding that averaging, but I suggest that a methodical
averaging process would have squeezed the most out of the available
information.

As I side-issue, I've come across a procedure, adopted by some, when faced
with the problem of reducing the scatter in a large number of observations
of what ought to be the same thing, by doing the following-

Reduce the range by rejecting both the highest and the lowest observation in
the set, as a pair, before averaging the rest. Indeed, if there are
sufficient observations, weed out the next remaing pair in the same way, and
so on. In the extreme, this shifts the resulting answer towards the median
of the set, rather than the mean. If the distribution is truly Gaussian, it
does nothing to reduce the scatter, compared with simple averaging of the
whole set; indeed, it increases it, though only slightly. The only virtue of
this approach appears when the distribution is not truly Gaussian; if there
are more outliers, further out from the average than ordinary statistics
would predict (blunders, perhaps). Then a procedure such as this can provide
a methodical way of weeding out such blunders.

George.

contact George Huxtable at george@huxtable.u-net.com
or at +44 1865 820222 (from UK, 01865 820222)
or at 1 Sandy Lane, Southmoor, Abingdon, Oxon OX13 5HX, UK.


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