endog, exog, what?s that? Statsmodels is using endog and exog as names for the data, the observed variables that are used in an estimation problem. Other names that are often used in different statistical packages or text books are, for example, endog exog y x y variable x variable left hand side (LHS) right hand side (RHS) dependent variable independent variable regressand regressors outcome design response variable explanatory variable The usage is quite often domain and model specific; how

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

statsmodels.genmod.generalized_linear_model.GLMResults.resid_pearson static GLMResults.resid_pearson() [source]

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.moment TransfTwo_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.sandbox.regression.gmm.IVRegressionResults.cov_HC1 static IVRegressionResults.cov_HC1() See statsmodels.RegressionResults

statsmodels.sandbox.distributions.transformed.Transf_gen.nnlf Transf_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.miscmodels.count.PoissonOffsetGMLE.information PoissonOffsetGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.linear_model.RegressionResults.compare_lr_test RegressionResults.compare_lr_test(restricted, large_sample=False) [source] Likelihood ratio test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. large_sample : bool Flag

statsmodels.sandbox.regression.gmm.GMM class statsmodels.sandbox.regression.gmm.GMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds) [source] Class for estimation by Generalized Method of Moments needs to be subclassed, where the subclass defined the moment conditions momcond Parameters: endog : array endogenous variable, see notes exog : array array of exogenous variables, see notes instrument : array array of instruments, see notes nmoms : None or int nu

statsmodels.discrete.discrete_model.LogitResults.pvalues static LogitResults.pvalues()