# Generating normally distributed random numbers in Javascript

Here I introduce a method that can be used to generate random numbers drawn from a normal distribution. The code below uses the Box-Muller transform to make sure the numbers are normally distributed.

```
function boxMullerTransform() {
const u1 = Math.random();
const u2 = Math.random();
const z0 = Math.sqrt(-2.0 * Math.log(u1)) * Math.cos(2.0 * Math.PI * u2);
const z1 = Math.sqrt(-2.0 * Math.log(u1)) * Math.sin(2.0 * Math.PI * u2);
return { z0, z1 };
}
function getNormallyDistributedRandomNumber(mean, stddev) {
const { z0, _ } = boxMullerTransform();
return z0 * stddev + mean;
}
```

`z1`

isn’t used in this case, but I’ll leave it in in case `boxMullerTransform()`

is used another
place. This is how you can use `getNormallyDistributedRandomNumber()`

:

```
const generatedNumbers = []
const mean = 30.0;
const stddev = 2.0;
for (let i = 0; i < 100000; i += 1) {
generatedNumbers.push(getNormallyDistributedRandomNumber(mean, stddev))
}
const sum = generatedNumbers.reduce((acc, i) => acc += i);
const count = generatedNumbers.length;
const calculatedMean = sum/count;
console.log(calculatedMean);
```

`generatedNumbers`

is an array that contains the numbers. To check that the mean really is what we
set it to be I calculate the mean and print it to the console. The output is 29.986221608852734,
which is close enough.

You can find a fiddle here.