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Date:         Thu, 12 Jan 2006 16:32:49 -0800
Reply-To:     David L Cassell <davidlcassell@MSN.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         David L Cassell <davidlcassell@MSN.COM>
Subject:      Re: Monte carlo on Flutter Prediction Process.
In-Reply-To:  <200601121205.k0CBk7Ma031956@mailgw.cc.uga.edu>
Content-Type: text/plain; format=flowed

asimaliabbasi@GMAIL.COM wrote: >I have been asked to apply Monte Carlo on Flutter Prediction Process, >but I dont know much about it. Can any body help me in this regard?

Well, I'm sure you were given a bit more direction than what you say above. But the basic idea is one of repeated sampling from a data set and using that set of replicates to run through the process and see what sort of variability one can expect.

I can't be more specific until you write back to SAS-L and explain *exactly* what you are being asked to do.

One use of Monte Carlo methods that you have probably seen without realizing it is computing the area under the curve. Instead of working with the integral of a function y=f(x), you set up a rectangle (x from a to b, where these are the bounds for the problem; y from, say, 0 to something more than the max of the curve in this range - let's call it M). So the box has area (b-a)*(M-0). Now throw 10,000 random points in there. How many of them are below the line? 7,348 you tell me. Then our estimate of the area under the curve from a to b is

area = (b-a)*(M-0) * 7348/10000

Now you'll have a better idea of what this is, and you can think about what the goal of the Monte Carlo simulation really is. When you write back, I'll probably specify something using PROC SURVEYSELECT and by-processing. I'm really predictable.

HTH, David -- David L. Cassell mathematical statistician Design Pathways 3115 NW Norwood Pl. Corvallis OR 97330

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