Normally distributed Random numbers generator issue
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Commented: Veronica Taurino on 25 Mar 2021
I've used the randn function (setting rng(1)) and plotted the histogram:
the result I've got in Matlab 2020b was rather far from a normal distribution.
Increasing the amount of generated numbers helps a bit - but not enough.
Any ideas on how to get a more adequate, normally distributed, bumbers ?
Veronica Taurino on 24 Mar 2021
Edited: Veronica Taurino on 24 Mar 2021
The following (but also yours, from my pov) seems quite normal to me, why is it not ''enough'' normal for you?
number = 1000000
Jan on 24 Mar 2021
Edited: Jan on 24 Mar 2021
This is a perfect normal distribution of 500 values. With such a small set of data, a lot of noise is expected. Otherwise it would not be a random normal distribution.
So the only problem is, that you expect something else, which does not match the defition of normally distributed randomness.
So what do you need? Do you want a perfect normal distribution without randomness, e.g. by just shuffling the values?
Veronica Taurino on 25 Mar 2021
I totally agree with Jan. You have been confused by the shape of the data, perhaps? But for definition, it is a normal distribution (as Walter has pointed out, mean 0 and std 1). If you just want to change the shape, it is not randon anymore.
Jeff Miller on 24 Mar 2021
As the other posters have said, you aren't necessarily going to get an exactly normal-looking distribution if you generate the numbers randomly. But you can nonrandomly generate an exactly normal-looking set of numbers like this:
nPoints = 500;
cdfvals = (0.5:1:nPoints) / nPoints;
evenRand = norminv(cdfvals);
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