statsmodels.stats.power.TTestPower.solve_power TTestPower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, alternative='two-sided') [source] solve for any one parameter of the power of a one sample t-test for the one sample t-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. This test can also be used for a paired t-test, where effect size is defined in terms of the mean difference, and nobs is the number of p

statsmodels.robust.norms.TukeyBiweight.psi TukeyBiweight.psi(z) [source] The psi function for Tukey?s biweight estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z*(1 - (z/c)**2)**2 for |z| <= R psi(z) = 0 for |z| > R

statsmodels.stats.power.TTestPower.plot_power TTestPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha, then one cur

statsmodels.stats.power.TTestPower.power TTestPower.power(effect_size, nobs, alpha, df=None, alternative='two-sided') [source] Calculate the power of a t-test for one sample or paired samples. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is w

statsmodels.stats.power.TTestIndPower.solve_power TTestIndPower.solve_power(effect_size=None, nobs1=None, alpha=None, power=None, ratio=1.0, alternative='two-sided') [source] solve for any one parameter of the power of a two sample t-test for t-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be None, all others need numeric values Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviation.

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.stats.power.TTestIndPower.plot_power TTestIndPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha, then o

statsmodels.tsa.x13.x13_arima_select_order statsmodels.tsa.x13.x13_arima_select_order(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True) [source] Perform automatic seaonal ARIMA order identification using x12/x13 ARIMA. Parameters: endog : array-like, pandas.Series The series to model. It is best to use a pandas object with a DatetimeIndex or Peri

statsmodels.tsa.x13.x13_arima_analysis statsmodels.tsa.x13.x13_arima_analysis(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, retspec=False, speconly=False, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True) [source] Perform x13-arima analysis for monthly or quarterly data. Parameters: endog : array-like, pandas.Series The series to model. It is best to use a pandas object with a DatetimeIndex

statsmodels.tsa.vector_ar.var_model.VARResults class statsmodels.tsa.vector_ar.var_model.VARResults(endog, endog_lagged, params, sigma_u, lag_order, model=None, trend='c', names=None, dates=None) [source] Estimate VAR(p) process with fixed number of lags Parameters: endog : array endog_lagged : array params : array sigma_u : array lag_order : int model : VAR model instance trend : str {?nc?, ?c?, ?ct?} names : array-like List of names of the endogenous variables in order of appearance in e