statsmodels.stats.proportion.proportions_chisquare statsmodels.stats.proportion.proportions_chisquare(count, nobs, value=None) [source] test for proportions based on chisquare test Parameters: count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as count. value : None or float or

statsmodels.stats.proportion.proportion_confint statsmodels.stats.proportion.proportion_confint(count, nobs, alpha=0.05, method='normal') [source] confidence interval for a binomial proportion Parameters: count : int or array number of successes nobs : int total number of trials alpha : float in (0, 1) significance level, default 0.05 method : string in [?normal?] method to use for confidence interval, currently available methods : normal : asymptotic normal approximation agresti_

statsmodels.stats.proportion.binom_test_reject_interval statsmodels.stats.proportion.binom_test_reject_interval(value, nobs, alpha=0.05, alternative='two-sided') [source] rejection region for binomial test for one sample proportion The interval includes the end points of the rejection region. Parameters: value : float proportion under the Null hypothesis nobs : integer the number of trials or observations. Returns: x_low, x_upp : float lower and upper bound of rejection region

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.var_model.VARProcess.is_stable VARProcess.is_stable(verbose=False) [source] Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.stats.proportion.proportions_ztost statsmodels.stats.proportion.proportions_ztost(count, nobs, low, upp, prop_var='sample') [source] Equivalence test based on normal distribution Parameters: count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as count. low, upp : floa

statsmodels.genmod.families.links.Power.inverse Power.inverse(z) [source] Inverse of the power transform link function Parameters: `z` : array-like Value of the transformed mean parameters at p Returns: `p` : array Mean parameters Notes g^(-1)(z`) = z`**(1/`power)

statsmodels.stats.sandwich_covariance.cov_nw_panel statsmodels.stats.sandwich_covariance.cov_nw_panel(results, nlags, groupidx, weights_func=, use_correction='hac') [source] Panel HAC robust covariance matrix Assumes we have a panel of time series with consecutive, equal spaced time periods. Data is assumed to be in long format with time series of each individual stacked into one array. Panel can be unbalanced. Parameters: results : result instance result of a regression, uses results.mode

statsmodels.sandbox.regression.try_catdata.groupsstats_dummy statsmodels.sandbox.regression.try_catdata.groupsstats_dummy(y, x, nonseq=0) [source]

statsmodels.tsa.vector_ar.irf.IRAnalysis.err_band_sz2 IRAnalysis.err_band_sz2(orth=False, repl=1000, signif=0.05, seed=None, burn=100, component=None) [source] IRF Sims-Zha error band method 2. This method Does not assume symmetric error bands around mean. Parameters: orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : in