.. class:: NormalDist(mu=0.0, sigma=1.0)
Returns a new *NormalDist* object where *mu* represents the `arithmetic
- mean <https://en.wikipedia.org/wiki/Arithmetic_mean>`_ of data and *sigma*
+ mean <https://en.wikipedia.org/wiki/Arithmetic_mean>`_ and *sigma*
represents the `standard deviation
- <https://en.wikipedia.org/wiki/Standard_deviation>`_ of the data.
+ <https://en.wikipedia.org/wiki/Standard_deviation>`_.
If *sigma* is negative, raises :exc:`StatisticsError`.
:class:`NormalDist` Examples and Recipes
----------------------------------------
-A :class:`NormalDist` readily solves classic probability problems.
+:class:`NormalDist` readily solves classic probability problems.
For example, given `historical data for SAT exams
<https://blog.prepscholar.com/sat-standard-deviation>`_ showing that scores
return exp((x - self.mu)**2.0 / (-2.0*variance)) / sqrt(tau * variance)
def cdf(self, x):
- 'Cumulative density function: P(X <= x)'
+ 'Cumulative distribution function: P(X <= x)'
if not self.sigma:
raise StatisticsError('cdf() not defined when sigma is zero')
return 0.5 * (1.0 + erf((x - self.mu) / (self.sigma * sqrt(2.0))))
Y = NormalDist(100, 0)
with self.assertRaises(statistics.StatisticsError):
Y.pdf(90)
+ # Special values
+ self.assertEqual(X.pdf(float('-Inf')), 0.0)
+ self.assertEqual(X.pdf(float('Inf')), 0.0)
+ self.assertTrue(math.isnan(X.pdf(float('NaN'))))
def test_cdf(self):
NormalDist = statistics.NormalDist
Y = NormalDist(100, 0)
with self.assertRaises(statistics.StatisticsError):
Y.cdf(90)
+ # Special values
+ self.assertEqual(X.cdf(float('-Inf')), 0.0)
+ self.assertEqual(X.cdf(float('Inf')), 1.0)
+ self.assertTrue(math.isnan(X.cdf(float('NaN'))))
def test_properties(self):
X = statistics.NormalDist(100, 15)