statsmodels.regression.quantile_regression.QuantRegResults.mse_total static QuantRegResults.mse_total() [source]

statsmodels.stats.moment_helpers.mc2mvsk statsmodels.stats.moment_helpers.mc2mvsk(args) [source] convert central moments to mean, variance, skew, kurtosis

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.r2 static DynamicVAR.r2() [source] Returns the r-squared values.

statsmodels.tsa.arima_model.ARMAResults class statsmodels.tsa.arima_model.ARMAResults(model, params, normalized_cov_params=None, scale=1.0) [source] Class to hold results from fitting an ARMA model. Parameters: model : ARMA instance The fitted model instance params : array Fitted parameters normalized_cov_params : array, optional The normalized variance covariance matrix scale : float, optional Optional argument to scale the variance covariance matrix. Returns: **Attributes** : ai

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.T static DynamicVAR.T() [source] Number of time periods in results

statsmodels.tsa.arima_model.ARIMA.information ARIMA.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.linear_model.OLSResults.normalized_cov_params OLSResults.normalized_cov_params()

statsmodels.sandbox.distributions.transformed.squaretg statsmodels.sandbox.distributions.transformed.squaretg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, derivplus, der

Graphics Goodness of Fit Plots gofplots.qqplot(data[, dist, distargs, a, ...]) Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution. gofplots.qqline(ax, line[, x, y, dist, fmt]) Plot a reference line for a qqplot. gofplots.qqplot_2samples(data1, data2[, ...]) Q-Q Plot of two samples? quantiles. gofplots.ProbPlot(data[, dist, fit, ...]) Class for convenient construction of Q-Q, P-P, and probability plots. Boxplots boxplots.violinplot(data[, ax, labels, ...]) Make a v

statsmodels.regression.linear_model.OLSResults.HC3_se static OLSResults.HC3_se() See statsmodels.RegressionResults