statsmodels.sandbox.distributions.transformed.TransfTwo_gen.median TransfTwo_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.distribut

statsmodels.graphics.gofplots.qqplot statsmodels.graphics.gofplots.qqplot(data, dist=, distargs=(), a=0, loc=0, scale=1, fit=False, line=None, ax=None) [source] Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution. Can take arguments specifying the parameters for dist or fit them automatically. (See fit under Parameters.) Parameters: data : array-like 1d data array dist : A scipy.stats or statsmodels distribution Compare x against dist. The default is scipy.stats.dis

statsmodels.sandbox.stats.multicomp.GroupsStats.runbasic_old GroupsStats.runbasic_old(useranks=False) [source]

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

Time Series analysis tsa statsmodels.tsa contains model classes and functions that are useful for time series analysis. This currently includes univariate autoregressive models (AR), vector autoregressive models (VAR) and univariate autoregressive moving average models (ARMA). It also includes descriptive statistics for time series, for example autocorrelation, partial autocorrelation function and periodogram, as well as the corresponding theoretical properties of ARMA or related processes. It

statsmodels.discrete.discrete_model.BinaryModel.pdf BinaryModel.pdf(X) The probability density (mass) function of the model.

statsmodels.tsa.ar_model.ARResults.wald_test ARResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple of arrays in

statsmodels.tsa.arima_model.ARMA.loglike_kalman ARMA.loglike_kalman(params, set_sigma2=True) [source] Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.

statsmodels.genmod.generalized_linear_model.GLMResults.resid_pearson static GLMResults.resid_pearson() [source]

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.moment TransfTwo_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.