statsmodels.discrete.discrete_model.MultinomialResults.t_test MultinomialResults.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 :

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

statsmodels.sandbox.regression.gmm.IVGMM.score IVGMM.score(params, weights, epsilon=None, centered=True)

statsmodels.iolib.table.SimpleTable.insert_header_row SimpleTable.insert_header_row(rownum, headers, dec_below='header_dec_below') [source] Return None. Insert a row of headers, where headers is a sequence of strings. (The strings may contain newlines, to indicated multiline headers.)

statsmodels.sandbox.stats.multicomp.maxzerodown statsmodels.sandbox.stats.multicomp.maxzerodown(x) [source] find all up zero crossings and return the index of the highest Not used anymore >>> np.random.seed(12345) >>> x = np.random.randn(8) >>> x array([-0.20470766, 0.47894334, -0.51943872, -0.5557303 , 1.96578057, 1.39340583, 0.09290788, 0.28174615]) >>> maxzero(x) (4, array([1, 4])) no up-zero-crossing at end >>> np.random.seed(0) &

statsmodels.robust.robust_linear_model.RLMResults.f_test RLMResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. 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

statsmodels.iolib.summary2.Summary class statsmodels.iolib.summary2.Summary [source] Methods add_array(array[, align, float_format]) Add the contents of a Numpy array to summary table add_base(results[, alpha, float_format, ...]) Try to construct a basic summary instance. add_df(df[, index, header, float_format, align]) Add the contents of a DataFrame to summary table add_dict(d[, ncols, align, float_format]) Add the contents of a Dict to summary table add_text(string) Append a note to

statsmodels.sandbox.stats.multicomp.GroupsStats.runbasic GroupsStats.runbasic(useranks=False) [source]

statsmodels.discrete.discrete_model.NegativeBinomial.jac NegativeBinomial.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7

statsmodels.sandbox.regression.gmm.IVRegressionResults.tvalues static IVRegressionResults.tvalues() Return the t-statistic for a given parameter estimate.