statsmodels.regression.linear_model.OLSResults.wald_test OLSResults.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

statsmodels.regression.linear_model.OLSResults.summary2 OLSResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental summary function to summarize the regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default ti

statsmodels.regression.linear_model.OLSResults.t_test OLSResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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 arra

statsmodels.regression.linear_model.OLSResults.save OLSResults.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_dat

statsmodels.regression.linear_model.OLSResults.summary OLSResults.summary(yname=None, xname=None, title=None, alpha=0.05) 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: smry : Summary insta

statsmodels.regression.linear_model.OLSResults.remove_data OLSResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute is

statsmodels.regression.linear_model.OLSResults.outlier_test OLSResults.outlier_test(method='bonf', alpha=0.05) [source] Test observations for outliers according to method Parameters: method : str bonferroni : one-step correction sidak : one-step correction holm-sidak : holm : simes-hochberg : hommel : fdr_bh : Benjamini/Hochberg fdr_by : Benjamini/Yekutieli See statsmodels.stats.multitest.multipletests for details. alpha : float familywise error rate Returns: table : ndarray o

statsmodels.regression.linear_model.OLSResults.predict OLSResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data struct

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

statsmodels.regression.linear_model.OLSResults.load classmethod OLSResults.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 :