docs: improve README examples of stats/base/dists/negative-binomial

lib/node_modules/@stdlib/stats/base/dists/negative-binomial/README.md

@@ -106,10 +106,43 @@ var y = dist.pmf( 4.0 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var objectKeys = require( '@stdlib/utils/keys' );
 var negativeBinomial = require( '@stdlib/stats/base/dists/negative-binomial' );
 
-console.log( objectKeys( negativeBinomial ) );
+/*
+* Let's take an example of flipping a biased coin until getting 5 heads.
+* This situation can be modeled using a Negative Binomial distribution with r = 5 and p = 1/2.
+*/
+
+var r = 5.0;
+var p = 1/2;
+
+// Mean can be used to calculate the average number of trials needed to get 5 heads:
+console.log(negativeBinomial.mean(r, p));
+// => 5
+
+// PMF can be used to calculate the probability of getting heads on a specific trial (say on the 8th trial):
+console.log(negativeBinomial.pmf(8, r, p));
+// => ~0.06042480468749999
+
+// CDF can be used to calculate the probability up to a certain number of trials (say up to 8 trials):
+console.log(negativeBinomial.cdf(8, r, p));
+// => ~0.8665771484375
+
+// Quantile can be used to calculate the number of trials at which you can be 80% confident that the actual number will not exceed:
+console.log(negativeBinomial.quantile(0.8, r, p));
+// => 7
+
+// Standard deviation can be used to calculate the measure of the spread of trials around the mean:
+console.log(negativeBinomial.stdev(r, p));
+// => ~3.1622776601683795
+
+// Skewness can be used to calculate the asymmetry of the distribution of trials:
+console.log(negativeBinomial.skewness(r, p));
+// => ~0.9486832980505138
+
+// MGF can be used for more advanced statistical analyses and generating moments of the distribution:
+console.log(negativeBinomial.mgf(0.5, r, p));
+// => ~2277.5972997372114
 ```
 
 </section>

lib/node_modules/@stdlib/stats/base/dists/negative-binomial/examples/index.js

@@ -18,7 +18,40 @@
 
 'use strict';
 
-var objectKeys = require( '@stdlib/utils/keys' );
 var negativeBinomial = require( './../lib' );
 
-console.log( objectKeys( negativeBinomial ) );
+/*
+* Let's take an example of flipping a biased coin until getting 5 heads.
+* This situation can be modeled using a Negative Binomial distribution with r = 5 and p = 1/2.
+*/
+
+var r = 5.0;
+var p = 1/2;
+
+// Mean can be used to calculate the average number of trials needed to get 5 heads:
+console.log(negativeBinomial.mean(r, p));
+// => 5
+
+// PMF can be used to calculate the probability of getting heads on a specific trial (say on the 8th trial):
+console.log(negativeBinomial.pmf(8, r, p));
+// => ~0.06042480468749999
+
+// CDF can be used to calculate the probability up to a certain number of trials (say up to 8 trials):
+console.log(negativeBinomial.cdf(8, r, p));
+// => ~0.8665771484375
+
+// Quantile can be used to calculate the number of trials at which you can be 80% confident that the actual number will not exceed:
+console.log(negativeBinomial.quantile(0.8, r, p));
+// => 7
+
+// Standard deviation can be used to calculate the measure of the spread of trials around the mean:
+console.log(negativeBinomial.stdev(r, p));
+// => ~3.1622776601683795
+
+// Skewness can be used to calculate the asymmetry of the distribution of trials:
+console.log(negativeBinomial.skewness(r, p));
+// => ~0.9486832980505138
+
+// MGF can be used for more advanced statistical analyses and generating moments of the distribution:
+console.log(negativeBinomial.mgf(0.5, r, p));
+// => ~2277.5972997372114