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improve README examples of stats/base/dists/binomial namespace #1748

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Mar 7, 2024
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improve README examples of stats/base/dists/binomial namespace #1748

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docs(examples): add examples to docs of binomial distribution
nishant-s7 Mar 7, 2024
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Planeshifter Mar 7, 2024
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37 changes: 35 additions & 2 deletions lib/node_modules/@stdlib/stats/base/dists/binomial/README.md
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Expand Up @@ -107,10 +107,43 @@ var mu = dist.mean;
<!-- eslint no-undef: "error" -->

```javascript
var objectKeys = require( '@stdlib/utils/keys' );
var binomial = require( '@stdlib/stats/base/dists/binomial' );

console.log( objectKeys( binomial ) );
/*
* Let's take an example of rolling a fair dice 10 times and counting the number of times a 6 is rolled.
* This situation can be modeled using a Binomial distribution with n = 10 and p = 1/6
*/

var n = 10;
var p = 1/6;

// Mean can be used to calculate the average number of times a 6 is rolled:
console.log( binomial.mean( n, p ) );
// => ~1.6667

// PMF can be used to calculate the probability of getting a certain number of 6s (say 3 sixes):
console.log( binomial.pmf( 3, n, p ) );
// => ~0.1550

// CDF can be used to calculate probability upto certain number of 6s (say upto 3 sixes):
console.log( binomial.cdf( 3, n, p ) );
// => ~0.9303

// Quantile can be used to calculate the number of 6s at which you can be 80% confident that the actual number will not exceed.
console.log( binomial.quantile( 0.8, n, p ) );
// => 3

// Standard deviation can be used to calculate the measure of the spread of 6s around the mean:
console.log( binomial.stdev( n, p ) );
// => ~1.1785

// Skewness can be used to calculate the asymmetry of the distribution of 6s:
console.log( binomial.skewness( n, p ) );
// => ~0.5657

// MGF can be used for more advanced statistical analyses and generating moments of the distribution:
console.log( binomial.mgf( 0.5, n, p ) );
// => ~2.7917
```

</section>
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37 changes: 35 additions & 2 deletions lib/node_modules/@stdlib/stats/base/dists/binomial/examples/index.js
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Expand Up @@ -18,7 +18,40 @@

'use strict';

var objectKeys = require( '@stdlib/utils/keys' );
var binomial = require( './../lib' );

console.log( objectKeys( binomial ) );
/*
* Let's take an example of rolling a fair dice 10 times and counting the number of times a 6 is rolled.
* This situation can be modeled using a Binomial distribution with n = 10 and p = 1/6
*/

var n = 10;
var p = 1/6;

// Mean can be used to calculate the average number of times a 6 is rolled:
console.log( binomial.mean( n, p ) );
// => ~1.6667

// PMF can be used to calculate the probability of getting a certain number of 6s (say 3 sixes):
console.log( binomial.pmf( 3, n, p ) );
// => ~0.1550

// CDF can be used to calculate probability upto certain number of 6s (say upto 3 sixes):
console.log( binomial.cdf( 3, n, p ) );
// => ~0.9303

// Quantile can be used to calculate the number of 6s at which you can be 80% confident that the actual number will not exceed.
console.log( binomial.quantile( 0.8, n, p ) );
// => 3

// Standard deviation can be used to calculate the measure of the spread of 6s around the mean:
console.log( binomial.stdev( n, p ) );
// => ~1.1785

// Skewness can be used to calculate the asymmetry of the distribution of 6s:
console.log( binomial.skewness( n, p ) );
// => ~0.5657

// MGF can be used for more advanced statistical analyses and generating moments of the distribution:
console.log( binomial.mgf( 0.5, n, p ) );
// => ~2.7917
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