statsmodels.sandbox.stats.multicomp.StepDown.run StepDown.run(alpha) [source] main function to run the test, could be done in __call__ instead this could have all the initialization code

statsmodels.sandbox.stats.multicomp.StepDown.check_set StepDown.check_set(indices) [source] check whether pairwise distances of indices satisfy condition

statsmodels.sandbox.stats.multicomp.line statsmodels.sandbox.stats.multicomp.line = '' str(object=??) -> string Return a nice string representation of the object. If the argument is a string, the return value is the same object.

statsmodels.tsa.arima_model.ARMAResults.bse static ARMAResults.bse() [source]

statsmodels.discrete.discrete_model.MultinomialResults.normalized_cov_params MultinomialResults.normalized_cov_params()

statsmodels.sandbox.regression.gmm.IVGMMResults.llf static IVGMMResults.llf()

statsmodels.stats.moment_helpers.mvsk2mnc statsmodels.stats.moment_helpers.mvsk2mnc(args) [source] convert mean, variance, skew, kurtosis to non-central moments

Generalized Linear Models (Formula) Link to Notebook GitHub This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models. To begin, we load the Star98 dataset and we construct a formula and pre-process the data: In [1]: from __future__ import print_function import statsmodels.api as sm import statsmodels.formula.api as smf star98 = sm.datasets.star98.load_pandas().data formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \

statsmodels.stats.proportion.power_binom_tost statsmodels.stats.proportion.power_binom_tost(low, upp, nobs, p_alt=None, alpha=0.05) [source]

statsmodels.regression.quantile_regression.QuantRegResults.initialize QuantRegResults.initialize(model, params, **kwd)