statsmodels.sandbox.distributions.extras.NormExpan_gen.interval NormExpan_gen.interval(alpha, *args, **kwds) Confidence interval with equal areas around the median. Parameters: alpha : array_like of float Probability that an rv will be drawn from the returned range. Each value should be in the range [0, 1]. arg1, arg2, ... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional location parameter,

statsmodels.discrete.discrete_model.Poisson.predict Poisson.predict(params, exog=None, exposure=None, offset=None, linear=False) Predict response variable of a count model given exogenous variables. Notes If exposure is specified, then it will be logged by the method. The user does not need to log it first.

statsmodels.sandbox.distributions.transformed.Transf_gen.logsf Transf_gen.logsf(x, *args, **kwds) Log of the survival function of the given RV. Returns the log of the ?survival function,? defined as (1 - cdf), evaluated at x. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.logcdf TransfTwo_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution function at x of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: logcdf : array

statsmodels.stats.proportion.proportions_ztest statsmodels.stats.proportion.proportions_ztest(count, nobs, value=None, alternative='two-sided', prop_var=False) [source] test for proportions based on normal (z) test Parameters: count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as

statsmodels.sandbox.distributions.transformed.Transf_gen.mean Transf_gen.mean(*args, **kwds) Mean of the distribution Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: mean : float the mean of the distribution

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.entropy TransfTwo_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional Location parameter (default=0). scale : array_like, optional Scale parameter (default=1).

statsmodels.genmod.families.links.nbinom class statsmodels.genmod.families.links.nbinom(alpha=1.0) [source] The negative binomial link function. Notes g(p) = log(p/(p + 1/alpha)) nbinom is an alias of NegativeBinomial. nbinom = NegativeBinomial(alpha=1.) Methods deriv(p) Derivative of the negative binomial transform inverse(z) Inverse of the negative binomial transform inverse_deriv(z) Derivative of the inverse of the negative binomial transform

statsmodels.robust.robust_linear_model.RLMResults.fittedvalues static RLMResults.fittedvalues() [source]

statsmodels.genmod.families.links.Logit.deriv2 Logit.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation