static NegativeBinomialResults.llf()

statsmodels.discrete.discrete_model.NegativeBinomialResults.llf static NegativeBinomialResults.llf()

static IVRegressionResults.condition_number()

statsmodels.sandbox.regression.gmm.IVRegressionResults.condition_number static IVRegressionResults.condition_number() Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue.

MixedLMResults.t_test()

statsmodels.regression.mixed_linear_model.MixedLMResults.t_test MixedLMResults.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

static ProbitResults.llr()

statsmodels.discrete.discrete_model.ProbitResults.llr static ProbitResults.llr()

IVGMMResults.t_test()

statsmodels.sandbox.regression.gmm.IVGMMResults.t_test IVGMMResults.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 a

static NegativeBinomialResults.resid()

statsmodels.discrete.discrete_model.NegativeBinomialResults.resid static NegativeBinomialResults.resid() Residuals Notes The residuals for Count models are defined as where . Any exposure and offset variables are also handled.

static GLMResults.pvalues()

statsmodels.genmod.generalized_linear_model.GLMResults.pvalues static GLMResults.pvalues()

static BinaryResults.tvalues()

statsmodels.discrete.discrete_model.BinaryResults.tvalues static BinaryResults.tvalues() Return the t-statistic for a given parameter estimate.

static BinaryResults.llr()

statsmodels.discrete.discrete_model.BinaryResults.llr static BinaryResults.llr()

ARMAResults.load()

statsmodels.tsa.arima_model.ARMAResults.load classmethod ARMAResults.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 :