'solve for any one parameter of the power of a one sample chisquare-test
for the one sample chisquare-test the keywords are:
effect_size, nobs, alpha, power
Exactly one needs to be ``None``, all others need numeric values.
n_bins needs to be defined, a default=2 is used.
Parameters
effect_size : float
standardized effect size, according to Cohen\'s definition.
see :func:`statsmodels.stats.gof.chisquare_effectsize`
nobs : int or float
sample size, number of observations.
alpha : float in interval (0,1)
significance level, e.g. 0.05, is the probability of a type I
error, that is wrong rejections if the Null Hypothesis is true.
power : float in interval (0,1)
power of the test, e.g. 0.8, is one minus the probability of a
type II error. Power is the probability that the test correctly
rejects the Null Hypothesis if the Alternative Hypothesis is true.
n_bins : int
number of bins or cells in the distribution
Returns
value : float
The value of the parameter that was set to None in the call. The
value solves the power equation given the remaining parameters.
Notes
The function uses scipy.optimize for finding the value that satisfies
the power equation. It first uses ``brentq`` with a prior search for
bounds. If this fails to find a root, ``fsolve`` is used. If ``fsolve``
also fails, then, for ``alpha``, ``power`` and ``effect_size``,
``brentq`` with fixed bounds is used. However, there can still be cases
where this fails.'

def solve_power(self, effect_size=None, nobs=None, alpha=None, power=None, n_bins=2):

    return super(GofChisquarePower, self).solve_power(effect_size=effect_size, nobs=nobs, n_bins=n_bins, alpha=alpha, power=power)
