statsmodels.tsa.arima_model.ARIMAResults.summary ARIMAResults.summary(alpha=0.05) Summarize the Model Parameters: alpha : float, optional Significance level for the confidence intervals. Returns: smry : Summary instance This holds the summary table and text, which can be printed or converted to various output formats. See also statsmodels.iolib.summary.Summary

statsmodels.regression.quantile_regression.QuantRegResults.nobs static QuantRegResults.nobs()

statsmodels.genmod.families.links.identity.inverse_deriv identity.inverse_deriv(z) Derivative of the inverse of the power transform Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the power transform : function :

statsmodels.genmod.families.family.Gaussian.resid_anscombe Gaussian.resid_anscombe(endog, mu) [source] The Anscombe residuals for the Gaussian exponential family distribution Parameters: endog : array Endogenous response variable mu : array Fitted mean response variable Returns: resid_anscombe : array The Anscombe residuals for the Gaussian family defined below Notes resid_anscombe = endog - mu

statsmodels.discrete.discrete_model.Probit.cov_params_func_l1 Probit.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.discrete.discrete_model.CountResults.resid static CountResults.resid() [source] Residuals Notes The residuals for Count models are defined as where . Any exposure and offset variables are also handled.

statsmodels.robust.norms.AndrewWave.weights AndrewWave.weights(z) [source] Andrew?s wave weighting function for the IRLS algorithm The psi function scaled by z Parameters: z : array-like 1d array Returns: weights : array weights(z) = sin(z/a)/(z/a) for |z| <= a*pi weights(z) = 0 for |z| > a*pi

statsmodels.stats.power.TTestIndPower.power TTestIndPower.power(effect_size, nobs1, alpha, ratio=1, df=None, alternative='two-sided') [source] Calculate the power of a t-test for two independent sample Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviation. effect_size has to be positive. nobs1 : int or float number of observations of sample 1. The number of observations of sample two is ratio times the size of sample

statsmodels.discrete.discrete_model.MNLogit.from_formula classmethod MNLogit.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args :

statsmodels.stats.stattools.durbin_watson statsmodels.stats.stattools.durbin_watson(resids, axis=0) [source] Calculates the Durbin-Watson statistic Parameters: resids : array-like Returns: dw : float, array-like The Durbin-Watson statistic. : Notes The null hypothesis of the test is that there is no serial correlation. The Durbin-Watson test statistics is defined as: The test statistic is approximately equal to 2*(1-r) where r is the sample autocorrelation of the residuals. Thus, for