# scipy.stats.nct¶

scipy.stats.nct = <scipy.stats.distributions.nct_gen object at 0x44f5ed0>[source]

A non-central Student’s T continuous random variable.

Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:

Parameters : x : array_like quantiles q : array_like lower or upper tail probability df, nc : array_like shape parameters loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) size : int or tuple of ints, optional shape of random variates (default computed from input arguments ) moments : str, optional composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’) Alternatively, the object may be called (as a function) to fix the shape, : location, and scale parameters returning a “frozen” continuous RV object: : rv = nct(df, nc, loc=0, scale=1) : Frozen RV object with the same methods but holding the given shape, location, and scale fixed.

Notes

The probability density function for nct is:

```                                    df**(df/2) * gamma(df+1)
nct.pdf(x, df, nc) = ----------------------------------------------------
2**df*exp(nc**2/2) * (df+x**2)**(df/2) * gamma(df/2)```

for df > 0, nc > 0.

Examples

```>>> from scipy.stats import nct
>>> numargs = nct.numargs
>>> [ df, nc ] = [0.9,] * numargs
>>> rv = nct(df, nc)
```

Display frozen pdf

```>>> x = np.linspace(0, np.minimum(rv.dist.b, 3))
>>> h = plt.plot(x, rv.pdf(x))
```

Here, rv.dist.b is the right endpoint of the support of rv.dist.

Check accuracy of cdf and ppf

```>>> prb = nct.cdf(x, df, nc)
>>> h = plt.semilogy(np.abs(x - nct.ppf(prb, df, nc)) + 1e-20)
```

Random number generation

```>>> R = nct.rvs(df, nc, size=100)
```

Methods

 rvs(df, nc, loc=0, scale=1, size=1) Random variates. pdf(x, df, nc, loc=0, scale=1) Probability density function. logpdf(x, df, nc, loc=0, scale=1) Log of the probability density function. cdf(x, df, nc, loc=0, scale=1) Cumulative density function. logcdf(x, df, nc, loc=0, scale=1) Log of the cumulative density function. sf(x, df, nc, loc=0, scale=1) Survival function (1-cdf — sometimes more accurate). logsf(x, df, nc, loc=0, scale=1) Log of the survival function. ppf(q, df, nc, loc=0, scale=1) Percent point function (inverse of cdf — percentiles). isf(q, df, nc, loc=0, scale=1) Inverse survival function (inverse of sf). moment(n, df, nc, loc=0, scale=1) Non-central moment of order n stats(df, nc, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(df, nc, loc=0, scale=1) (Differential) entropy of the RV. fit(data, df, nc, loc=0, scale=1) Parameter estimates for generic data. expect(func, df, nc, 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, nc, loc=0, scale=1) Median of the distribution. mean(df, nc, loc=0, scale=1) Mean of the distribution. var(df, nc, loc=0, scale=1) Variance of the distribution. std(df, nc, loc=0, scale=1) Standard deviation of the distribution. interval(alpha, df, nc, loc=0, scale=1) Endpoints of the range that contains alpha percent of the distribution

scipy.stats.ncf

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scipy.stats.pareto