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

statsmodels.sandbox.regression.gmm.LinearIVGMM.momcond LinearIVGMM.momcond(params)

statsmodels.tsa.arima_model.ARMA.geterrors ARMA.geterrors(params) [source] Get the errors of the ARMA process. Parameters: params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend

Linear Mixed Effects Models Linear Mixed Effects models are used for regression analyses involving dependent data. Such data arise when working with longitudinal and other study designs in which multiple observations are made on each subject. Two specific mixed effects models are ?random intercepts models?, where all responses in a single group are additively shifted by a value that is specific to the group, and ?random slopes models?, where the values follow a mean trajectory that is linear in

statsmodels.iolib.summary.Summary.as_text Summary.as_text() [source] return tables as string Returns: txt : string summary tables and extra text as one string

statsmodels.discrete.discrete_model.LogitResults.summary LogitResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns:

statsmodels.tsa.arima_model.ARMAResults.plot_predict ARMAResults.plot_predict(start=None, end=None, exog=None, dynamic=False, alpha=0.05, plot_insample=True, ax=None) [source] Plot forecasts Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first for

statsmodels.stats.weightstats._zstat_generic statsmodels.stats.weightstats._zstat_generic(value1, value2, std_diff, alternative, diff=0) [source] generic (normal) z-test to save typing can be used as ztest based on summary statistics

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

statsmodels.tsa.arima_model.ARIMAResults.bic static ARIMAResults.bic()