statsmodels.stats.proportion.proportions_chisquare statsmodels.stats.proportion.proportions_chisquare(count, nobs, value=None) [source] test for proportions based on chisquare test Parameters: count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as count. value : None or float or

statsmodels.stats.proportion.power_ztost_prop statsmodels.stats.proportion.power_ztost_prop(low, upp, nobs, p_alt, alpha=0.05, dist='norm', variance_prop=None, discrete=True, continuity=0, critval_continuity=0) [source] Power of proportions equivalence test based on normal distribution Parameters: low, upp : floats lower and upper limit of equivalence region nobs : int number of observations p_alt : float in (0,1) proportion under the alternative alpha : float in (0,1) significance l

statsmodels.stats.proportion.power_binom_tost statsmodels.stats.proportion.power_binom_tost(low, upp, nobs, p_alt=None, alpha=0.05) [source]

statsmodels.stats.proportion.binom_tost_reject_interval statsmodels.stats.proportion.binom_tost_reject_interval(low, upp, nobs, alpha=0.05) [source] rejection region for binomial TOST The interval includes the end points, reject if and only if r_low <= x <= r_upp. The interval might be empty with r_upp < r_low. Parameters: low, upp : floats lower and upper limit of equivalence region nobs : integer the number of trials or observations. Returns: x_low, x_upp : float lower and

statsmodels.stats.proportion.binom_test_reject_interval statsmodels.stats.proportion.binom_test_reject_interval(value, nobs, alpha=0.05, alternative='two-sided') [source] rejection region for binomial test for one sample proportion The interval includes the end points of the rejection region. Parameters: value : float proportion under the Null hypothesis nobs : integer the number of trials or observations. Returns: x_low, x_upp : float lower and upper bound of rejection region

statsmodels.stats.proportion.binom_tost statsmodels.stats.proportion.binom_tost(count, nobs, low, upp) [source] exact TOST test for one proportion using binomial distribution Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. low, upp : floats lower and upper limit of equivalence region Returns: pvalue : float p-value of equivalence test pval_low, pval_upp : floats p-values of lower and upper one-

statsmodels.stats.proportion.binom_test statsmodels.stats.proportion.binom_test(count, nobs, prop=0.5, alternative='two-sided') [source] Perform a test that the probability of success is p. This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is p. Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. prop : float, optional The probability of suc

statsmodels.stats.power.tt_solve_power statsmodels.stats.power.tt_solve_power = > 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 pairs. Parameters: effect_size : float standardized effect size, mean d

statsmodels.stats.power.zt_ind_solve_power statsmodels.stats.power.zt_ind_solve_power = > solve for any one parameter of the power of a two sample z-test for z-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. If ratio=0, then this is the standardized mean in the one sample test. nobs1 : i

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