statsmodels.tools.tools.add_constant statsmodels.tools.tools.add_constant(data, prepend=True, has_constant='skip') [source] This appends a column of ones to an array if prepend==False. Parameters: data : array-like data is the column-ordered design matrix prepend : bool True and the constant is prepended rather than appended. has_constant : str {?raise?, ?add?, ?skip?} Behavior if ``data?? already has a constant. The default will return data without adding another constant. If ?raise?,

statsmodels.discrete.discrete_model.NegativeBinomialResults class statsmodels.discrete.discrete_model.NegativeBinomialResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for NegativeBinomial 1 and 2 Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : f

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

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

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.

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

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.

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

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

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