numpy.random.chisquare¶

numpy.random.
chisquare
(df, size=None)¶ Draw samples from a chisquare distribution.
When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chisquare (see Notes). This distribution is often used in hypothesis testing.
Parameters: df : int or array_like of ints
Number of degrees of freedom.
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifdf
is a scalar. Otherwise,np.array(df).size
samples are drawn.Returns: out : ndarray or scalar
Drawn samples from the parameterized chisquare distribution.
Raises: ValueError
When df <= 0 or when an inappropriate size (e.g.
size=1
) is given.Notes
The variable obtained by summing the squares of df independent, standard normally distributed random variables:
Q = \sum_{i=0}^{\mathtt{df}} X^2_i
is chisquare distributed, denoted
Q \sim \chi^2_k.
The probability density function of the chisquared distribution is
p(x) = \frac{(1/2)^{k/2}}{\Gamma(k/2)} x^{k/2  1} e^{x/2},
where \Gamma is the gamma function,
\Gamma(x) = \int_0^{\infty} t^{x  1} e^{t} dt.
References
[R213] NIST “Engineering Statistics Handbook” http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm Examples
>>> np.random.chisquare(2,4) array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])