statsmodels.regression.linear_model.OLSResults.ess static OLSResults.ess()

statsmodels.discrete.discrete_model.LogitResults.resid_dev static LogitResults.resid_dev() Deviance residuals Notes Deviance residuals are defined where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.discrete.discrete_model.LogitResults.load classmethod LogitResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.discrete.discrete_model.Probit.loglike Probit.loglike(params) [source] Log-likelihood of probit model (i.e., the normal distribution). Parameters: params : array-like The parameters of the model. Returns: loglike : float The log-likelihood function of the model evaluated at params. See notes. Notes Where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.discrete.discrete_model.LogitResults.remove_data LogitResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute

statsmodels.discrete.discrete_model.LogitResults.llnull static LogitResults.llnull()

statsmodels.discrete.discrete_model.Probit.cdf Probit.cdf(X) [source] Probit (Normal) cumulative distribution function Parameters: X : array-like The linear predictor of the model (XB). Returns: cdf : ndarray The cdf evaluated at X. Notes This function is just an alias for scipy.stats.norm.cdf

statsmodels.tsa.arima_process.arma2ma statsmodels.tsa.arima_process.arma2ma(ar, ma, nobs=100) get the impulse response function (MA representation) for ARMA process Parameters: ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns: ir : array, 1d impulse response function with nobs elements Notes This is the same as finding the MA representation of an ARMA(p,q). By reversing the

statsmodels.sandbox.stats.multicomp.MultiComparison.tukeyhsd MultiComparison.tukeyhsd(alpha=0.05) [source] Tukey?s range test to compare means of all pairs of groups Parameters: alpha : float, optional Value of FWER at which to calculate HSD. Returns: results : TukeyHSDResults instance A results class containing relevant data and some post-hoc calculations

statsmodels.sandbox.distributions.extras.SkewNorm_gen.median SkewNorm_gen.median(*args, **kwds) Median 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distributions.rv