### Navigation

- index
- modules |
- next |
- previous |
- NumPy v1.3 Manual (DRAFT) »
- NumPy Reference »
- Routines »
- Random sampling (`numpy.random`) »

# numpy.random.chisquare

`numpy.random.``chisquare`(df, size=None)

  Draw samples from a chi-square distribution.

  When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing.

  Parameters: | df : int Number of degrees of freedom. size : tuple of ints, int, optional Size of the returned array. By default, a scalar is returned.
  Returns: | output : ndarray Samples drawn from the distribution, packed in a size-shaped array.
  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:

  is chi-square distributed, denoted

  The probability density function of the chi-squared distribution is

  where is the gamma function,

  References

  [59] | NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm

  [60] | Wikipedia, “Chi-square distribution”, http://en.wikipedia.org/wiki/Chi-square_distribution

  Examples

      >>> np.random.chisquare(2,4)
      array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])

### Navigation

- index
- modules |
- next |
- previous |
- NumPy v1.3 Manual (DRAFT) »
- NumPy Reference »
- Routines »
- Random sampling (`numpy.random`) »