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improve README examples of stats/base/dists/poisson namespace #1734

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Mar 7, 2024
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improve README examples of stats/base/dists/poisson namespace #1734

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docs(examples): add examples to the documentation of poisson distribu…
nishant-s7 Mar 6, 2024
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Planeshifter Mar 7, 2024
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Update lib/node_modules/@stdlib/stats/base/dists/poisson/README.md
Planeshifter Mar 7, 2024
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36 changes: 34 additions & 2 deletions lib/node_modules/@stdlib/stats/base/dists/poisson/README.md
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Expand Up @@ -111,10 +111,42 @@ y = dist.pmf( 2.3 );
<!-- eslint no-undef: "error" -->

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

console.log( objectKeys( poisson ) );
/*
* Let's take a customer service center example: average rate of customer inquiries is 3 per hour.
* This situation can be modeled using a Poisson distribution with λ = 3
*/

var lambda = 3;

// Mean can be used to calculate the average number of inquiries per hour:
console.log( poisson.mean( lambda ) );
// => 3

// Standard deviation can be used to calculate the measure of the spread of inquiries around the mean:
console.log( poisson.stdev( lambda ) );
// => ~1.7321

// Variance can be used to calculate the variability of the number of inquiries:
console.log( poisson.variance( lambda ) );
// => 3

// PMF can be used to calculate specific number of inquiries in an hour:
console.log( poisson.pmf( 4, lambda ) );
// => ~0.1680

// CDF can be used to calculate probability upto certain number of inquiries in an hour
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console.log( poisson.cdf( 2, lambda ) );
// => ~0.4232

// Quantile can be used to calculate the number of inquiries at which you can be 80% confident that the actual number will not exceed.
console.log( poisson.quantile( 0.8, lambda ) );
// => 4

// MGF can be used for more advanced statistical analyses and generating moments of the distribution.
console.log( poisson.mgf( 1.0, lambda ) );
// => ~173.2690
```

</section>
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36 changes: 34 additions & 2 deletions lib/node_modules/@stdlib/stats/base/dists/poisson/examples/index.js
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Expand Up @@ -18,7 +18,39 @@

'use strict';

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

console.log( objectKeys( poisson ) );
/*
* Let's take a customer service center example: average rate of customer inquiries is 3 per hour.
* This situation can be modeled using a Poisson distribution with λ = 3
*/

var lambda = 3;

// Mean can be used to calculate the average number of inquiries per hour:
console.log( poisson.mean( lambda ) );
// => 3

// Standard deviation can be used to calculate the measure of the spread of inquiries around the mean:
console.log( poisson.stdev( lambda ) );
// => ~1.7321

// Variance can be used to calculate the variability of the number of inquiries:
console.log( poisson.variance( lambda ) );
// => 3

// PMF can be used to calculate specific number of inquiries in an hour:
console.log( poisson.pmf( 4, lambda ) );
// => ~0.1680

// CDF can be used to calculate probability upto certain number of inquiries in an hour:
console.log( poisson.cdf( 2, lambda ) );
// => ~0.4232

// Quantile can be used to calculate the number of inquiries at which you can be 80% confident that the actual number will not exceed.
console.log( poisson.quantile( 0.8, lambda ) );
// => 4

// MGF can be used for more advanced statistical analyses and generating moments of the distribution.
console.log( poisson.mgf( 1.0, lambda ) );
// => ~173.2690
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