statsmodels.emplike.descriptive.DescStatMV.mv_mean_contour DescStatMV.mv_mean_contour(mu1_low, mu1_upp, mu2_low, mu2_upp, step1, step2, levs=[0.2, 0.1, 0.05, 0.01, 0.001], var1_name=None, var2_name=None, plot_dta=False) [source] Creates a confidence region plot for the mean of bivariate data Parameters: m1_low : float Minimum value of the mean for variable 1 m1_upp : float Maximum value of the mean for variable 1 mu2_low : float Minimum value of the mean for variable 2 mu2_upp : float

statsmodels.emplike.descriptive.DescStatMV.ci_corr DescStatMV.ci_corr(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence intervals for the correlation coefficient Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value the upper confidence limit can be. Default is 99% confidence limit assuming normality. lower_bound : float Minimum value the lower condidence limit can be. Default is 99% confidence limit assuming normal

statsmodels.stats.weightstats.DescrStatsW.ztest_mean DescrStatsW.ztest_mean(value=0, alternative='two-sided') [source] z-test of Null hypothesis that mean is equal to value. The alternative hypothesis H1 is defined by the following ?two-sided?: H1: mean not equal to value ?larger? : H1: mean larger than value ?smaller? : H1: mean smaller than value Parameters: value : float or array the hypothesized value for the mean alternative : string The alternative hypothesis, H1, has to be one of

statsmodels.stats.weightstats.DescrStatsW.zconfint_mean DescrStatsW.zconfint_mean(alpha=0.05, alternative='two-sided') [source] two-sided confidence interval for weighted mean of data Confidence interval is based on normal distribution. If the data is 2d, then these are separate confidence intervals for each column. Parameters: alpha : float significance level for the confidence interval, coverage is 1-alpha alternative : string This specifies the alternative hypothesis for the test that

statsmodels.stats.weightstats.DescrStatsW.ztost_mean DescrStatsW.ztost_mean(low, upp) [source] test of (non-)equivalence of one sample, based on z-test TOST: two one-sided z-tests null hypothesis: m < low or m > upp alternative hypothesis: low < m < upp where m is the expected value of the sample (mean of the population). If the pvalue is smaller than a threshold, say 0.05, then we reject the hypothesis that the expected value of the sample (mean of the population) is outside of

statsmodels.stats.weightstats.DescrStatsW.ttest_mean DescrStatsW.ttest_mean(value=0, alternative='two-sided') [source] ttest of Null hypothesis that mean is equal to value. The alternative hypothesis H1 is defined by the following ?two-sided?: H1: mean not equal to value ?larger? : H1: mean larger than value ?smaller? : H1: mean smaller than value Parameters: value : float or array the hypothesized value for the mean alternative : string The alternative hypothesis, H1, has to be one of t

statsmodels.stats.weightstats.DescrStatsW.ttost_mean DescrStatsW.ttost_mean(low, upp) [source] test of (non-)equivalence of one sample TOST: two one-sided t tests null hypothesis: m < low or m > upp alternative hypothesis: low < m < upp where m is the expected value of the sample (mean of the population). If the pvalue is smaller than a threshold, say 0.05, then we reject the hypothesis that the expected value of the sample (mean of the population) is outside of the interval give

statsmodels.stats.weightstats.DescrStatsW.var_ddof DescrStatsW.var_ddof(ddof=0) [source] variance of data given ddof Parameters: ddof : int, float degrees of freedom correction, independent of attribute ddof Returns: var : float, ndarray variance with denominator sum_weights - ddof

statsmodels.stats.weightstats.DescrStatsW.std_ddof DescrStatsW.std_ddof(ddof=0) [source] standard deviation of data with given ddof Parameters: ddof : int, float degrees of freedom correction, independent of attribute ddof Returns: std : float, ndarray standard deviation with denominator sum_weights - ddof

statsmodels.stats.weightstats.DescrStatsW.tconfint_mean DescrStatsW.tconfint_mean(alpha=0.05, alternative='two-sided') [source] two-sided confidence interval for weighted mean of data If the data is 2d, then these are separate confidence intervals for each column. Parameters: alpha : float significance level for the confidence interval, coverage is 1-alpha alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternat