DOC: another stab at the cumulative hist doc

phobson · phobson · commit 1da445b0088b · 2016-10-24T08:43:02.000-07:00
diff --git a/examples/statistics/histogram_demo_cumulative.py b/examples/statistics/histogram_demo_cumulative.py
@@ -10,23 +10,23 @@
 
 A couple of other options to the ``hist`` function are demonstrated.
 Namely, we use the ``normed`` parameter to normalize the histogram and
-a couple of different options to the ``cumulative`` parameter. Normalizing
-a histogram means that the bin heights are scaled such that
-the total area is 1. Since we're showing a normalized and
-cumulative histogram, the max value at the end of the series is 1.
-The ``normed`` parameter takes a boolean value.
-
-The ``cumulative`` kwarg is a little more nuanced. Like ``normed``, you
-can pass it True or False, but you can also pass it -1 and that will
-reverse the distribution. In engineering, CDFs where ``cumulative`` is
-simply True are sometimes "non-exceedance" curves. In other words, you
-can look at the y-value to set the probability of exceedance. For
-example the value of 225 on the x-axis corresponds to about 0.85 on the
-y-axis, so there's an 85% chance that an observation in the sample does
-not exceed 225.
-
-Conversely, setting, ``cumulative`` to -1 as is done in the last series
-for this example, creates a "exceedance" curve.
+a couple of different options to the ``cumulative`` parameter.
+The ``normed`` parameter takes a boolean value. When ``True``, the bin
+heights are scaled such that the total area of the histogram is 1. The
+``cumulative`` kwarg is a little more nuanced. Like ``normed``, you
+can pass it True or False, but you can also pass it -1 to reverse the
+distribution.
+
+Since we're showing a normalized and cumulative histogram, these curves
+are effectively the cumulative distribution functions (CDFs) of the
+samples. In engineering, empirical CDFs where are sometimes called
+"non-exceedance" curves. In other words, you can look at the
+y-value for a given-x-value to get the probability of and observation
+from the sample not exceeding that x-value. For example, the value of
+225 on the x-axis corresponds to about 0.85 on the y-axis, so there's an
+85% chance that an observation in the sample does not exceed 225.
+Conversely, setting, ``cumulative`` to -1 as is done in the
+last series for this example, creates a "exceedance" curve.
 
 Selecting different bin counts and sizes can significantly affect the
 shape of a histogram. The Astropy docs have a great section on how to
