This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Perform Kolmogorov-Smirnov Test

MuPAD® notebooks are not recommended. Use MATLAB® live scripts instead.

MATLAB live scripts support most MuPAD functionality, though there are some differences. For more information, see Convert MuPAD Notebooks to MATLAB Live Scripts.

For the Kolmogorov-Smirnov goodness-of-fit test, MuPAD® provides the stats::ksGOFT function. This function enables you to test the data against any cumulative distribution available in the MuPAD Statistics library. The Kolmogorov-Smirnov test returns two p-values. The null hypothesis passes the test only if both values are larger than the significance level. For example, create the following data sequence x which contains a thousand entries:

f := stats::normalRandom(1, 1/3):
x := f() $ k = 1..1000:

Use the function stats::ksGOFT to test whether the sequence x has a normal distribution with the mean 1 and the variance 1/3. Suppose, you apply the typical significance level 0.05. Since both p-values are larger than the significance level, the sequence passes the test:

stats::ksGOFT(x, CDF = stats::normalCDF(1, 1/3))

Test the same sequence, but this time compare it to the normal distribution with the variance 1. Both p-values are much smaller than the significance level. The null hypothesis states that the sequence x has a normal distribution with the mean 1 and the variance 1. This hypothesis must be rejected:

stats::ksGOFT(x, CDF = stats::normalCDF(1, 1))

Was this topic helpful?