Path: news.mathworks.com!not-for-mail 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) Organization: Università del Salento Lines: 21 Message-ID: <ki9dra$56c$1@newscl01ah.mathworks.com> Reply-To: "Francesco Perrone" <francesco86perrone@yahoo.it> NNTP-Posting-Host: www-06-blr.mathworks.com Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: 8bit X-Trace: newscl01ah.mathworks.com 1363688107 5324 172.30.248.38 (19 Mar 2013 10:15:07 GMT) X-Complaints-To: news@mathworks.com NNTP-Posting-Date: Tue, 19 Mar 2013 10:15:07 +0000 (UTC) X-Newsreader: MATLAB Central Newsreader 1737773 Xref: news.mathworks.com comp.soft-sys.matlab:791470 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.