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## Perform Shapiro-Wilk Test

MuPAD® notebooks will be removed in a future release. 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.

The Shapiro-Wilk goodness-of-fit test asserts the hypothesis that the data has a normal distribution. For the Shapiro-Wilk goodness-of-fit test, MuPAD® provides the `stats::swGOFT` function. For example, create the normally distributed data sequence `x` by using the `stats::normalRandom` function:

```fx := stats::normalRandom(0, 1/2): x := fx() \$ k = 1..1000:```

Also, create the data sequence `y` by using the `stats::poissonRandom` function. This function generates random numbers according to the Poisson distribution:

```fy := stats::poissonRandom(10): y := fy() \$ k = 1..1000:```

Now, use the `stats::swGOFT` function to test whether these two sequences are normally distributed. Suppose, you use the typical significance level 0.05. For the first sequence (`x`), the resulting p-value is above the significance level. The entries of the sequence `x` can have a normal distribution. For the second sequence (`y`), the resulting p-value is below the significance level. Therefore, reject the hypothesis that the entries of this sequence are normally distributed:

```stats::swGOFT(x); stats::swGOFT(y)```

#### Mathematical Modeling with Symbolic Math Toolbox

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