statsmodels.stats.weightstats.CompareMeans.ttest_ind CompareMeans.ttest_ind(alternative='two-sided', usevar='pooled', value=0) [source] ttest for the null hypothesis of identical means this should also be the same as onewaygls, except for ddof differences Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following ?two-sided?: H1: difference in means not equal to value (def

statsmodels.emplike.descriptive.DescStatUV.ci_mean DescStatUV.ci_mean(sig=0.05, method='gamma', epsilon=1e-08, gamma_low=-10000000000, gamma_high=10000000000) [source] Returns the confidence interval for the mean. Parameters: sig : float significance level. Default is .05 method : str Root finding method, Can be ?nested-brent? or ?gamma?. Default is ?gamma? ?gamma? Tries to solve for the gamma parameter in the Lagrange (see Owen pg 22) and then determine the weights. ?nested brent? uses

statsmodels.stats.diagnostic.het_white statsmodels.stats.diagnostic.het_white(resid, exog, retres=False) White?s Lagrange Multiplier Test for Heteroscedasticity Parameters: resid : array_like residuals, square of it is used as endogenous variable exog : array_like possible explanatory variables for variance, squares and interaction terms are included in the auxilliary regression. resstore : instance (optional) a class instance that holds intermediate results. Only returned if store=Tru

statsmodels.genmod.families.family.Family.predict Family.predict(mu) [source] Linear predictors based on given mu values. Parameters: mu : array The mean response variables Returns: lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu.

statsmodels.emplike.descriptive.DescStatUV.test_kurt DescStatUV.test_kurt(kurt0, return_weights=False) [source] Returns -2 x log-likelihood and the p-value for the hypothesized kurtosis. Parameters: kurt0 : float Kurtosis value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple The log-likelihood ratio and p-value of kurt0

statsmodels.stats.proportion.binom_tost_reject_interval statsmodels.stats.proportion.binom_tost_reject_interval(low, upp, nobs, alpha=0.05) [source] rejection region for binomial TOST The interval includes the end points, reject if and only if r_low <= x <= r_upp. The interval might be empty with r_upp < r_low. Parameters: low, upp : floats lower and upper limit of equivalence region nobs : integer the number of trials or observations. Returns: x_low, x_upp : float lower and

statsmodels.tsa.vector_ar.irf.IRAnalysis class statsmodels.tsa.vector_ar.irf.IRAnalysis(model, P=None, periods=10, order=None, svar=False) [source] Impulse response analysis class. Computes impulse responses, asymptotic standard errors, and produces relevant plots Parameters: model : VAR instance Notes Using Lutkepohl (2005) notation Methods G() H() cov([orth]) Compute asymptotic standard errors for impulse response coefficients cum_effect_cov([orth]) Compute asymptotic standard error

statsmodels.genmod.cov_struct.Nested.update Nested.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.genmod.cov_struct.Nested.initialize Nested.initialize(model) [source] Called on the first call to update ilabels is a list of n_i x n_i matrices containing integer labels that correspond to specific correlation parameters. Two elements of ilabels[i] with the same label share identical variance components. designx is a matrix, with each row containing dummy variables indicating which variance components are associated with the corresponding element of QY.

statsmodels.sandbox.stats.multicomp.MultiComparison.tukeyhsd MultiComparison.tukeyhsd(alpha=0.05) [source] Tukey?s range test to compare means of all pairs of groups Parameters: alpha : float, optional Value of FWER at which to calculate HSD. Returns: results : TukeyHSDResults instance A results class containing relevant data and some post-hoc calculations