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docs: improve examples stats/base/dists/triangular namespace
PR-URL: #1952 Closes: #1646 --------- Co-authored-by: Philipp Burckhardt <[email protected]> Reviewed-by: Philipp Burckhardt <[email protected]>
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‎lib/node_modules/@stdlib/stats/base/dists/triangular/README.md

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@@ -107,15 +107,62 @@ y = dist.quantile( 1.9 );
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## Examples
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<!-- TODO: better examples -->
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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 discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
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var triangular = require( '@stdlib/stats/base/dists/triangular' );
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console.log( objectKeys( triangular ) );
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// Scenario: Modeling completion time for a software development task
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// Define the distribution parameters (in hours):
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var a = 1.5; // Minimum time (best-case scenario)
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var b = 4.5; // Maximum time (worst-case scenario)
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var c = discreteUniform( 2, 4 ); // Most likely time (mode)
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console.log( 'a: %d, b: %d, c: %d', a, b, c );
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// Expected (mean) completion time:
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var mean = triangular.mean( a, b, c );
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console.log( '\nExpected completion time: %d hours', mean );
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// Median completion time:
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var median = triangular.median( a, b, c );
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console.log( 'Median completion time: %d hours', median );
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// Variance in completion time:
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var variance = triangular.variance( a, b, c );
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console.log( 'Variance in completion time: %d hours^2', variance );
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// Probability of completing the task within 3 hours:
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var x = 3.0;
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var prob = triangular.cdf( x, a, b, c );
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console.log( '\nProbability of completing within %d hours: %d', x, prob );
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// 90th percentile of completion time:
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var p = 0.9;
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var percentile = triangular.quantile( p, a, b, c );
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console.log( '90% of tasks will be completed within %d hours', percentile );
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// Relative likelihood of completing the task in exactly 2.5 hours:
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x = 2.5;
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var likelihood = triangular.pdf( x, a, b, c );
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console.log( '\nRelative likelihood of completing in exactly %d hours: %d', x, likelihood );
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// Skewness to understand the distribution's shape:
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var skewness = triangular.skewness( a, b, c );
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console.log( '\nSkewness of completion times: %d', skewness );
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if ( skewness > 0 ) {
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console.log( 'The distribution is right-skewed, suggesting occasional longer completion times.' );
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} else if ( skewness < 0 ) {
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console.log( 'The distribution is left-skewed, suggesting occasional shorter completion times.' );
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} else {
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console.log( 'The distribution is symmetric.' );
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}
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// Entropy as a measure of uncertainty in the estimate:
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var entropy = triangular.entropy( a, b, c );
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console.log( '\nEntropy of the distribution: %d nats', entropy );
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console.log( 'Higher entropy indicates more uncertainty in completion times.' );
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```
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</section>

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

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'use strict';
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var objectKeys = require( '@stdlib/utils/keys' );
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var discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
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var triangular = require( './../lib' );
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console.log( objectKeys( triangular ) );
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// Scenario: Modeling completion time for a software development task
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// Define the distribution parameters (in hours):
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var a = 1.5; // Minimum time (best-case scenario)
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var b = 4.5; // Maximum time (worst-case scenario)
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var c = discreteUniform( 2, 4 ); // Most likely time (mode)
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console.log( 'a: %d, b: %d, c: %d', a, b, c );
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// Expected (mean) completion time:
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var mean = triangular.mean( a, b, c );
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console.log( '\nExpected completion time: %d hours', mean );
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// Median completion time:
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var median = triangular.median( a, b, c );
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console.log( 'Median completion time: %d hours', median );
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// Variance in completion time:
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var variance = triangular.variance( a, b, c );
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console.log( 'Variance in completion time: %d hours^2', variance );
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// Probability of completing the task within 3 hours:
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var x = 3.0;
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var prob = triangular.cdf( x, a, b, c );
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console.log( '\nProbability of completing within %d hours: %d', x, prob );
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// 90th percentile of completion time:
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var p = 0.9;
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var percentile = triangular.quantile( p, a, b, c );
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console.log( '90% of tasks will be completed within %d hours', percentile );
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// Relative likelihood of completing the task in exactly 2.5 hours:
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x = 2.5;
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var likelihood = triangular.pdf( x, a, b, c );
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console.log( '\nRelative likelihood of completing in exactly %d hours: %d', x, likelihood );
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// Skewness to understand the distribution's shape:
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var skewness = triangular.skewness( a, b, c );
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console.log( '\nSkewness of completion times: %d', skewness );
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if ( skewness > 0 ) {
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console.log( 'The distribution is right-skewed, suggesting occasional longer completion times.' );
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} else if ( skewness < 0 ) {
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console.log( 'The distribution is left-skewed, suggesting occasional shorter completion times.' );
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} else {
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console.log( 'The distribution is symmetric.' );
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}
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// Entropy as a measure of uncertainty in the estimate:
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var entropy = triangular.entropy( a, b, c );
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console.log( '\nEntropy of the distribution: %d nats', entropy );
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console.log( 'Higher entropy indicates more uncertainty in completion times.' );

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