statsmodels.tsa.arima_model.ARMAResults.forecast ARMAResults.forecast(steps=1, exog=None, alpha=0.05) [source] Out-of-sample forecasts Parameters: steps : int The number of out of sample forecasts from the end of the sample. exog : array If the model is an ARMAX, you must provide out of sample values for the exogenous variables. This should not include the constant. alpha : float The confidence intervals for the forecasts are (1 - alpha) % Returns: forecast : array Array of out of

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.isf ExpTransf_gen.isf(q, *args, **kwds) Inverse survival function at q of the given RV. Parameters: q : array_like upper tail probability 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: x : ndarray or scalar Qu

statsmodels.graphics.boxplots.violinplot statsmodels.graphics.boxplots.violinplot(data, ax=None, labels=None, positions=None, side='both', show_boxplot=True, plot_opts={}) [source] Make a violin plot of each dataset in the data sequence. A violin plot is a boxplot combined with a kernel density estimate of the probability density function per point. Parameters: data : sequence of ndarrays Data arrays, one array per value in positions. ax : Matplotlib AxesSubplot instance, optional If giv

statsmodels.sandbox.regression.gmm.IVRegressionResults.normalized_cov_params IVRegressionResults.normalized_cov_params()

statsmodels.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.discrete.discrete_model.Logit.hessian Logit.hessian(params) [source] Logit model Hessian matrix of the log-likelihood Parameters: params : array-like The parameters of the model Returns: hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at params Notes

statsmodels.stats.proportion.binom_tost_reject_interval statsmodels.stats.proportion.binom_tost_reject_interval(low, upp, nobs, alpha=0.05) [source] rejection region for binomial TOST The interval includes the end points, reject if and only if r_low <= x <= r_upp. The interval might be empty with r_upp < r_low. Parameters: low, upp : floats lower and upper limit of equivalence region nobs : integer the number of trials or observations. Returns: x_low, x_upp : float lower and

statsmodels.stats.proportion.binom_test_reject_interval statsmodels.stats.proportion.binom_test_reject_interval(value, nobs, alpha=0.05, alternative='two-sided') [source] rejection region for binomial test for one sample proportion The interval includes the end points of the rejection region. Parameters: value : float proportion under the Null hypothesis nobs : integer the number of trials or observations. Returns: x_low, x_upp : float lower and upper bound of rejection region

statsmodels.discrete.discrete_model.ProbitResults.summary ProbitResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns

statsmodels.sandbox.distributions.extras.ACSkewT_gen.logsf ACSkewT_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 sca