If I store your data in the variable, x, then
histfit(x,30,'normal');
produces a plot that shows a normal distribution is a pretty good fit.
You can also look at the a qqplot
qqplot(x)
which shows some slight deviation from a normal distribution in the tails, but not bad.
Finally, you can use
[muhat,sigmahat] = normfit(x);
to estimate the parameters of a Gaussian distribution for the data.
If you want to plot the pdf
xval = 0:.01:max(x);
[muhat,sigmahat] = normfit(x);
yval = normpdf(xval,muhat,sigmahat);
plot(xval,yval)
muhat is the estimate of the mean of the Gaussian and sigmahat, the standard deviation of the Gaussian. Finally, to illustrate the use of ksdensity
[f,xi] = ksdensity(x);
plot(xi,f)
