Add cum_weights example (simulation of a cumulative binomial distribution).
diff --git a/Doc/library/random.rst b/Doc/library/random.rst
index a47ed9c..edf76d7 100644
--- a/Doc/library/random.rst
+++ b/Doc/library/random.rst
@@ -351,6 +351,13 @@
>>> choices(['red', 'black', 'green'], [18, 18, 2], k=6)
['red', 'green', 'black', 'black', 'red', 'black']
+ # Probability of getting 5 or more heads from 7 spins of a biased coin
+ # that settles on heads 60% of the time.
+ >>> n = 10000
+ >>> cw = [0.60, 1.00]
+ >>> sum(choices('HT', cum_weights=cw, k=7).count('H') >= 5 for i in range(n)) / n
+ 0.4169
+
Example of `statistical bootstrapping
<https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling
with replacement to estimate a confidence interval for the mean of a small