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From: "Francesco Perrone" <francesco86perrone@yahoo.it>
Newsgroups: comp.soft-sys.matlab
Subject: manipulate data to better fit a Gaussian Distribution
Date: Tue, 19 Mar 2013 10:15:07 +0000 (UTC)
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Hi all,

I have got a question concerning normal distribution (with mu = 0 and sigma = 1).

Let say that I firstly call randn or normrnd this way

x = normrnd(0,1,[4096,1]); % x = randn(4096,1)

Now, to assess how good x values fit the normal distribution, I call

[a,b] = normfit(x);

and to have a graphical support

histfit(x)

Now come to the core of the question: if I am not satisfied enough on how x fits the given normal distribution, how can I optimize x in order to better fit the expected normal distribution with 0 mean and 1 standard deviation?? Sometimes because of the few representation values (i.e. 4096 in this case), x fits really poorly the expected Gaussian, so that I wanna manipulate x (linearly or not, it does not really matter at this stage) in order to get a better fitness.

I'd like remarking that I have access to the statistical toolbox. 

I thank you all in advance.