statsmodels.stats.weightstats.CompareMeans.dof_satt CompareMeans.dof_satt() [source] degrees of freedom of Satterthwaite for unequal variance

statsmodels.iolib.summary2.Summary.add_text Summary.add_text(string) [source] Append a note to the bottom of the summary table. In ASCII tables, the note will be wrapped to table width. Notes are not indendented.

statsmodels.duration.hazard_regression.PHReg.score PHReg.score(params) [source] Returns the score function evaluated at params.

statsmodels.robust.robust_linear_model.RLMResults.wald_test RLMResults.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 tup

statsmodels.duration.hazard_regression.PHRegResults.schoenfeld_residuals static PHRegResults.schoenfeld_residuals() [source] A matrix containing the Schoenfeld residuals. Notes Schoenfeld residuals for censored observations are set to zero.

Pitfalls This page lists issues which may arise while using statsmodels. These can be the result of data-related or statistical problems, software design, ?non-standard? use of models, or edge cases. statsmodels provides several warnings and helper functions for diagnostic checking (see this blog article for an example of misspecification checks in linear regression). The coverage is of course not comprehensive, but more warnings and diagnostic functions will be added over time. While the under

statsmodels.duration.hazard_regression.PHRegResults.martingale_residuals static PHRegResults.martingale_residuals() [source] The martingale residuals.

statsmodels.emplike.descriptive.DescStatUV.test_var DescStatUV.test_var(sig2_0, return_weights=False) [source] Returns -2 x log-likelihoog ratio and the p-value for the hypothesized variance Parameters: sig2_0 : float Hypothesized variance to be tested return_weights : bool If True, returns the weights that maximize the likelihood of observing sig2_0. Default is False Returns: test_results : tuple The log-likelihood ratio and the p_value of sig2_0 Examples >>> random_numbe

statsmodels.emplike.descriptive.DescStatUV.ci_mean DescStatUV.ci_mean(sig=0.05, method='gamma', epsilon=1e-08, gamma_low=-10000000000, gamma_high=10000000000) [source] Returns the confidence interval for the mean. Parameters: sig : float significance level. Default is .05 method : str Root finding method, Can be ?nested-brent? or ?gamma?. Default is ?gamma? ?gamma? Tries to solve for the gamma parameter in the Lagrange (see Owen pg 22) and then determine the weights. ?nested brent? uses

statsmodels.genmod.generalized_linear_model.GLMResults.wald_test GLMResults.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 :