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docs: improve README examples of stats/base/dists/poisson namespace
PR-URL: stdlib-js#1734 Closes: stdlib-js#1641 --------- Signed-off-by: Philipp Burckhardt <pburckhardt@outlook.com> Co-authored-by: Philipp Burckhardt <pburckhardt@outlook.com> Reviewed-by: Philipp Burckhardt <pburckhardt@outlook.com>
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‎lib/node_modules/@stdlib/stats/base/dists/poisson/README.md‎

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@@ -111,10 +111,42 @@ y = dist.pmf( 2.3 );
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<!-- eslint no-undef: "error" -->
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```javascript
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var objectKeys = require( '@stdlib/utils/keys' );
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var poisson = require( '@stdlib/stats/base/dists/poisson' );
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console.log( objectKeys( poisson ) );
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/*
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* Let's take a customer service center example: average rate of customer inquiries is 3 per hour.
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* This situation can be modeled using a Poisson distribution with λ = 3
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*/
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var lambda = 3;
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// Mean can be used to calculate the average number of inquiries per hour:
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console.log( poisson.mean( lambda ) );
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// => 3
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// Standard deviation can be used to calculate the measure of the spread of inquiries around the mean:
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console.log( poisson.stdev( lambda ) );
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// => ~1.7321
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// Variance can be used to calculate the variability of the number of inquiries:
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console.log( poisson.variance( lambda ) );
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// => 3
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// PMF can be used to calculate specific number of inquiries in an hour:
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console.log( poisson.pmf( 4, lambda ) );
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// => ~0.1680
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// 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 ) );
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// => ~0.4232
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// Quantile can be used to calculate the number of inquiries at which you can be 80% confident that the actual number will not exceed.
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console.log( poisson.quantile( 0.8, lambda ) );
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// => 4
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// MGF can be used for more advanced statistical analyses and generating moments of the distribution.
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console.log( poisson.mgf( 1.0, lambda ) );
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// => ~173.2690
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```
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</section>

‎lib/node_modules/@stdlib/stats/base/dists/poisson/examples/index.js‎

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'use strict';
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var objectKeys = require( '@stdlib/utils/keys' );
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var poisson = require( './../lib' );
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console.log( objectKeys( poisson ) );
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/*
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* Let's take a customer service center example: average rate of customer inquiries is 3 per hour.
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* This situation can be modeled using a Poisson distribution with λ = 3
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*/
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var lambda = 3;
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// Mean can be used to calculate the average number of inquiries per hour:
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console.log( poisson.mean( lambda ) );
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// => 3
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// Standard deviation can be used to calculate the measure of the spread of inquiries around the mean:
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console.log( poisson.stdev( lambda ) );
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// => ~1.7321
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// Variance can be used to calculate the variability of the number of inquiries:
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console.log( poisson.variance( lambda ) );
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// => 3
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// PMF can be used to calculate specific number of inquiries in an hour:
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console.log( poisson.pmf( 4, lambda ) );
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// => ~0.1680
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// 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 ) );
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// => ~0.4232
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// Quantile can be used to calculate the number of inquiries at which you can be 80% confident that the actual number will not exceed.
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console.log( poisson.quantile( 0.8, lambda ) );
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// => 4
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// MGF can be used for more advanced statistical analyses and generating moments of the distribution.
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console.log( poisson.mgf( 1.0, lambda ) );
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// => ~173.2690

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