How to test my data against an specific normal distribution? I need to test my data to see if it follows a normal distribution with specific mean and std like N~(mu, std) I know that this can be done by  Kolmogorov-Smirnov test which has a function in both MATLAB and R but the default for these function is standard normal and I do not know how should I specify my mu and std in the function. If you can help me in any of these two softwares I will be thankful.
 A: In R, you can just use the function ks.test with the following arguments:
ks.test(your_data, "pnorm", mean=test_mu, sd=test_sd)

Where your_data is your data vector, test_mu is the specific mean of the theoretical normal distribution and test_sd its standard deviation.
To inspect your data graphically, you can use the function qqPlot from the car package. Just use it with the following arguments:
qqPlot(your_data, "norm", mean=test_mu, sd=test_sd)

This produces a Q-Q plot with a comparison line and a 95% point-wise confidence envelope (as default).
Hope that helps.
A: So don't use the default! As already noted, R lets you specify the population mean and standard deviation.
Here is how to do it in MATLAB or anything else that doesn't give you the option to specify: 
Standardize your variable by the population parameters: $z_i = \frac{x_i-\mu_0}{\sigma_0}$
... and then test against standard normal.
(However, if those $\mu$ and $\sigma$ values come from a sample... don't do this!)
A: ks.test in R allows one to adjust the mean and sd of the distribution to be tested against. e.g.
x <- rnorm(1000, 4, 10)
ks.test(x, "pnorm", mean = 4, sd = 10)

