statsmodels.graphics.regressionplots.plot_regress_exog statsmodels.graphics.regressionplots.plot_regress_exog(results, exog_idx, fig=None) [source] Plot regression results against one regressor. This plots four graphs in a 2 by 2 figure: ?endog versus exog?, ?residuals versus exog?, ?fitted versus exog? and ?fitted plus residual versus exog? Parameters: results : result instance result instance with resid, model.endog and model.exog as attributes exog_idx : int index of regressor in exog

statsmodels.tsa.arima_model.ARIMAResults.wald_test ARIMAResults.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 a

statsmodels.sandbox.regression.gmm.IVRegressionResults.spec_hausman IVRegressionResults.spec_hausman(dof=None) [source] Hausman?s specification test See also spec_hausman generic function for Hausman?s specification test

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.distributions.transformed.Transf_gen.cdf Transf_gen.cdf(x, *args, **kwds) Cumulative distribution function 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: cdf : ndarray Cumulative distribution fun

statsmodels.duration.hazard_regression.PHRegResults.pvalues static PHRegResults.pvalues()

statsmodels.regression.linear_model.OLSResults.conf_int OLSResults.conf_int(alpha=0.05, cols=None) Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return Notes The confidence interval is based on Student?s t-distribution.

statsmodels.tsa.vector_ar.var_model.VARProcess.forecast_cov VARProcess.forecast_cov(steps) Compute theoretical forecast error variance matrices Parameters: steps : int Number of steps ahead Returns: forc_covs : ndarray (steps x neqs x neqs) Notes

statsmodels.sandbox.distributions.extras.NormExpan_gen.moment NormExpan_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.

statsmodels.emplike.descriptive.DescStatUV.ci_kurt DescStatUV.ci_kurt(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence interval for kurtosis. Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value of kurtosis the upper limit can be. Default is .99 confidence limit assuming normality. lower_bound : float Minimum value of kurtosis the lower limit can be. Default is .99 confidence limit assuming normality. Returns: