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mathman

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chiro

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I'm curious about this one-sided thing and in some circumstances, this may not be a good idea. Can you tell us the context of your problem and what you are trying to do overall?

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Just to clarify, there's a difference between the variance of the data and the estimated variance of the population. To get the variance of the data (just as a collection of numbers), you divide the sum square (value-mean) by N, the number of datapoints. To get the unbiased estimate of the variance of the population you divide by N-1 instead.

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I have tried using the least mean square error approach but the differential equations of the Gaussian get a bit messy.

here is an example of the data I'm trying to get the Gaussian of best fit to

https://www.dropbox.com/s/mb6ebbdmqq7y0xm/cov_ukv_high_30_frm_112011_to_112012.jpg

- #6

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I have tried using the least mean square error approach but the differential equations of the Gaussian get a bit messy.

here is an example of the data I'm trying to get the Gaussian of best fit to

https://www.dropbox.com/s/mb6ebbdmqq7y0xm/cov_ukv_high_30_frm_112011_to_112012.jpg

That doesn't strike as a particularly "Gaussian" distribution. Honestly, fitting something like a Weibull distribution seems like a better bet to me for a few reasons, not the least of which because it's strictly positive. The stat toolbox in matlab should let you fit the distribution using maximum likelihood estimation.

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The problem is I have an extremely limited number of separations between the radar I'm using which massively limits the number of points I can plot

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