the real question is, 'how much accuracy (precision, variance) is
suitable?'
If you were to repeat the simulation run (i.e., a test) a total of n
times, then you could say that the true mean elapsed time was x-bar +/-
(certain amount), with say 95% confidence.
That is, if you were to then repeat the whole process, n times again, 95%
of the time the x-bar would fall within the +/- (certain amount) you had
calculated. The average of your mean elapsed time is probably Normal, so
this equation can be used. If you want to predict the one next elapsed
time from the next simulation run, then you have to believe that your
individual times are Normally distributed, or do some deeper analysis.
If that's confusing, I'm sorry, but it comes from what you asked.
You can do the simulation run n times, and _estimate_ a value for mean
elapsed time that could be confirmed only by say 100*n runs. Does this
sound like what you want?
The eq. for the 'certain amount' is given by
certain amount = s*z/sqrt(n)
where s = stdev of your n run times, z = 1.96 for 95% confidence, and n =
number of simulation runs.
Pick a confidence interval ('certain amount') that you like, then solve
for n to decide how many runs you will need to make. Statistics cannot
tell you what confidence interval is suitable to your problem - that is a
technical issue. It can tell you now many n's you need to reach that
confidence interval.
Is this what you were looking for?
Cheers,
Jay
PS: Yes, I know 'accuracy' and 'precision' refer to different things.
But you used the first of these words in a way which I infer meant the
latter, so I opened the first sentence in that manner.
Gooseman wrote:
> Hi,
>
> I am writing a computer simulation, and I really would appreciate some
> advice about statistics side of things!
>
> Each simulation run has fixed settings, but there is some randomness
> involved (e.g. start position). As a result, each simulation scenario
> needs to be run until the universal mean (say time taken for objective
> to be met) varience is reduced.
>
> The simulation has just one output that needs measurement - time
> taken, and there is no transient state.
>
> The question is, what accuracy is acceptable, and how can I guartee
> that the varience is small enough to be accurate, while being
> efficient on computing power. Any methods, techniques etc. gladly
> welcome, as I am new to stats!!
>
> Thanks.
>
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--
Jay Warner
Principal Scientist
Warner Consulting, Inc.
4444 North Green Bay Road
Racine, WI 53404-1216
USA
Ph: (262) 634-9100
FAX: (262) 681-1133
email: [EMAIL PROTECTED]
web: http://www.a2q.com
The A2Q Method (tm) -- What do you want to improve today?
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