statsmodels.regression.linear_model.OLSResults.resid static OLSResults.resid()

statsmodels.graphics.gofplots.ProbPlot.sample_quantiles static ProbPlot.sample_quantiles() [source]

statsmodels.tsa.arima_process.ArmaProcess.arma2ar ArmaProcess.arma2ar(nobs=None) [source]

statsmodels.tsa.arima_process.lpol_fiar statsmodels.tsa.arima_process.lpol_fiar(d, n=20) [source] AR representation of fractional integration Parameters: d : float fractional power n : int number of terms to calculate, including lag zero Returns: ar : array coefficients of lag polynomial Notes: : first coefficient is 1, negative signs except for first term, : ar(L)*x_t :

statsmodels.discrete.discrete_model.BinaryResults.remove_data BinaryResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribu

statsmodels.robust.robust_linear_model.RLMResults.conf_int RLMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval.

statsmodels.tsa.vector_ar.var_model.VARProcess.acorr VARProcess.acorr(nlags=None) [source] Compute theoretical autocorrelation function Returns: acorr : ndarray (p x k x k)

statsmodels.stats.proportion.samplesize_confint_proportion statsmodels.stats.proportion.samplesize_confint_proportion(proportion, half_length, alpha=0.05, method='normal') [source] find sample size to get desired confidence interval length Parameters: proportion : float in (0, 1) proportion or quantile half_length : float in (0, 1) desired half length of the confidence interval alpha : float in (0, 1) significance level, default 0.05, coverage of the two-sided interval is (approximatel

statsmodels.genmod.generalized_estimating_equations.GEE.cluster_list GEE.cluster_list(array) [source] Returns array split into subarrays corresponding to the cluster structure.

statsmodels.genmod.generalized_estimating_equations.GEEMargins.conf_int GEEMargins.conf_int(alpha=0.05) [source] Returns the confidence intervals of the marginal effects Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: conf_int : ndarray An array with lower, upper confidence intervals for the marginal effects.