how to perform a kolmogorov-smirnov test in matlab

To perform a Kolmogorov-Smirnov test for goodness of fit in Matlab, you can use the built-in function kstest. The kstest function tests whether the sample data comes from a specified continuous distribution, or from a normal distribution with a specified mean and standard deviation.

Here's an example use of the kstest function:

main.m
% Generate sample data from a normal distribution
mu = 0; % mean
sigma = 1; % standard deviation
sample_data = normrnd(mu, sigma, 100, 1);

% Perform a Kolmogorov-Smirnov test with the null hypothesis that the
% sample data comes from a normal distribution with mean=0 and std dev=1
[h, p, ksstat] = kstest(sample_data, 'norm', mu, sigma);

% Display the results
if h == 0
    fprintf('The sample is consistent with a normal distribution.\n');
else
    fprintf('The sample is not consistent with a normal distribution.\n');
end
fprintf('p-value: %f\n', p);
fprintf('KS statistic: %f\n', ksstat);
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In this example, the null hypothesis is that the sample data comes from a normal distribution with mean=0 and standard deviation=1. The kstest function returns a p-value, which we can use to make a decision about whether to reject the null hypothesis. If the p-value is less than the significance level (e.g. 0.05), we can reject the null hypothesis and conclude that the sample data is not consistent with the specified distribution.