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
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.