scipy.stats.chi2#

scipy.stats.chi2 = <scipy.stats._continuous_distns.chi2_gen object>[source]#

A chi-squared continuous random variable.

For the noncentral chi-square distribution, see ncx2.

As an instance of the rv_continuous class, chi2 object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.

See also

ncx2

Notes

The probability density function for chi2 is:

\[f(x, k) = \frac{1}{2^{k/2} \Gamma \left( k/2 \right)} x^{k/2-1} \exp \left( -x/2 \right)\]

for \(x > 0\) and \(k > 0\) (degrees of freedom, denoted df in the implementation).

chi2 takes df as a shape parameter.

The chi-squared distribution is a special case of the gamma distribution, with gamma parameters a = df/2, loc = 0 and scale = 2.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, chi2.pdf(x, df, loc, scale) is identically equivalent to chi2.pdf(y, df) / scale with y = (x - loc) / scale. Note that shifting the location of a distribution does not make it a “noncentral” distribution; noncentral generalizations of some distributions are available in separate classes.

Examples

>>> from scipy.stats import chi2
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate the first four moments:

>>> df = 55
>>> mean, var, skew, kurt = chi2.stats(df, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(chi2.ppf(0.01, df),
...                 chi2.ppf(0.99, df), 100)
>>> ax.plot(x, chi2.pdf(x, df),
...        'r-', lw=5, alpha=0.6, label='chi2 pdf')

Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pdf:

>>> rv = chi2(df)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = chi2.ppf([0.001, 0.5, 0.999], df)
>>> np.allclose([0.001, 0.5, 0.999], chi2.cdf(vals, df))
True

Generate random numbers:

>>> r = chi2.rvs(df, size=1000)

And compare the histogram:

>>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()

Methods

rvs(df, loc=0, scale=1, size=1, random_state=None)	Random variates.
pdf(x, df, loc=0, scale=1)	Probability density function.
logpdf(x, df, loc=0, scale=1)	Log of the probability density function.
cdf(x, df, loc=0, scale=1)	Cumulative distribution function.
logcdf(x, df, loc=0, scale=1)	Log of the cumulative distribution function.
sf(x, df, loc=0, scale=1)	Survival function (also defined as `1 - cdf`, but sf is sometimes more accurate).
logsf(x, df, loc=0, scale=1)	Log of the survival function.
ppf(q, df, loc=0, scale=1)	Percent point function (inverse of `cdf` — percentiles).
isf(q, df, loc=0, scale=1)	Inverse survival function (inverse of `sf`).
moment(n, df, loc=0, scale=1)	Non-central moment of order n
stats(df, loc=0, scale=1, moments=’mv’)	Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(df, loc=0, scale=1)	(Differential) entropy of the RV.
fit(data)	Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments.
expect(func, args=(df,), loc=0, scale=1, lb=None, ub=None, conditional=False, kwds)**	Expected value of a function (of one argument) with respect to the distribution.
median(df, loc=0, scale=1)	Median of the distribution.
mean(df, loc=0, scale=1)	Mean of the distribution.
var(df, loc=0, scale=1)	Variance of the distribution.
std(df, loc=0, scale=1)	Standard deviation of the distribution.
interval(alpha, df, loc=0, scale=1)	Endpoints of the range that contains fraction alpha [0, 1] of the distribution

scipy.stats.chi

scipy.stats.cosine