statsmodels.stats.power.TTestPower class statsmodels.stats.power.TTestPower(**kwds) [source] Statistical Power calculations for one sample or paired sample t-test Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs, alpha[, df, ...]) Calculate the power of a t-test for one sample or paired samples. solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a one sample t

statsmodels.stats.power.TTestIndPower class statsmodels.stats.power.TTestIndPower(**kwds) [source] Statistical Power calculations for t-test for two independent sample currently only uses pooled variance Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs1, alpha[, ratio, ...]) Calculate the power of a t-test for two independent sample solve_power([effect_size, nobs1, alpha, ...]) solve for any one p

statsmodels.stats.power.NormalIndPower class statsmodels.stats.power.NormalIndPower(ddof=0, **kwds) [source] Statistical Power calculations for z-test for two independent samples. currently only uses pooled variance Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs1, alpha[, ratio, ...]) Calculate the power of a t-test for two independent sample solve_power([effect_size, nobs1, alpha, ...]) solve f

statsmodels.stats.power.GofChisquarePower class statsmodels.stats.power.GofChisquarePower(**kwds) [source] Statistical Power calculations for one sample chisquare test Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs, alpha, n_bins[, ddof]) Calculate the power of a chisquare test for one sample solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a one sample ch

statsmodels.stats.power.FTestPower class statsmodels.stats.power.FTestPower(**kwds) [source] Statistical Power calculations for generic F-test Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, df_num, df_denom, alpha) Calculate the power of a F-test. solve_power([effect_size, df_num, df_denom, ...]) solve for any one parameter of the power of a F-test

statsmodels.stats.power.FTestAnovaPower class statsmodels.stats.power.FTestAnovaPower(**kwds) [source] Statistical Power calculations F-test for one factor balanced ANOVA Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs, alpha[, k_groups]) Calculate the power of a F-test for one factor ANOVA. solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a F-test

statsmodels.stats.outliers_influence.variance_inflation_factor statsmodels.stats.outliers_influence.variance_inflation_factor(exog, exog_idx) [source] variance inflation factor, VIF, for one exogenous variable The variance inflation factor is a measure for the increase of the variance of the parameter estimates if an additional variable, given by exog_idx is added to the linear regression. It is a measure for multicollinearity of the design matrix, exog. One recommendation is that if VIF is

statsmodels.stats.outliers_influence.OLSInfluence class statsmodels.stats.outliers_influence.OLSInfluence(results) [source] class to calculate outlier and influence measures for OLS result Parameters: results : Regression Results instance currently assumes the results are from an OLS regression Notes One part of the results can be calculated without any auxiliary regression (some of which have the _internal postfix in the name. Other statistics require leave-one-observation-out (LOOO) au

statsmodels.stats.multicomp.pairwise_tukeyhsd statsmodels.stats.multicomp.pairwise_tukeyhsd(endog, groups, alpha=0.05) [source] calculate all pairwise comparisons with TukeyHSD confidence intervals this is just a wrapper around tukeyhsd method of MultiComparison Parameters: endog : ndarray, float, 1d response variable groups : ndarray, 1d array with groups, can be string or integers alpha : float significance level for the test Returns: results : TukeyHSDResults instance A results

statsmodels.stats.moment_helpers.se_cov statsmodels.stats.moment_helpers.se_cov(cov) [source] get standard deviation from covariance matrix just a shorthand function np.sqrt(np.diag(cov)) Parameters: cov : array_like, square covariance matrix Returns: std : ndarray standard deviation from diagonal of cov