statsmodels.sandbox.distributions.transformed.LogTransf_gen.std LogTransf_gen.std(*args, **kwds) Standard deviation 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: std : float standard deviation of the distribution

statsmodels.stats.diagnostic.breaks_hansen statsmodels.stats.diagnostic.breaks_hansen(olsresults) test for model stability, breaks in parameters for ols, Hansen 1992 Parameters: olsresults : instance of RegressionResults uses only endog and exog Returns: teststat : float Hansen?s test statistic crit : structured array critical values at alpha=0.95 for different nvars pvalue Not yet : ft, s : arrays temporary return for debugging, will be removed Notes looks good in example, maybe

statsmodels.tsa.arima_model.ARMA class statsmodels.tsa.arima_model.ARMA(endog, order, exog=None, dates=None, freq=None, missing='none') [source] Autoregressive Moving Average ARMA(p,q) Model Parameters: endog : array-like The endogenous variable. order : iterable The (p,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should not include a constant or trend. You can spe

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.LogitResults.prsquared static LogitResults.prsquared()

statsmodels.tsa.arima_model.ARMAResults.wald_test ARMAResults.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 arr

statsmodels.discrete.discrete_model.LogitResults.save LogitResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove

statsmodels.nonparametric.kde.KDEUnivariate.evaluate KDEUnivariate.evaluate(point) [source] Evaluate density at a single point. Parameters: point : float Point at which to evaluate the density.

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.var TransfTwo_gen.var(*args, **kwds) Variance 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: var : float the variance of the distribution

statsmodels.stats.diagnostic.acorr_ljungbox statsmodels.stats.diagnostic.acorr_ljungbox(x, lags=None, boxpierce=False) Ljung-Box test for no autocorrelation Parameters: x : array_like, 1d data series, regression residuals when used as diagnostic test lags : None, int or array_like If lags is an integer then this is taken to be the largest lag that is included, the test result is reported for all smaller lag length. If lags is a list or array, then all lags are included up to the largest