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| ## data_a: forecast_value, data_b: pass_value | |
| def Welch_ttest(data_a, data_b, alpha): | |
| mean_a, mean_b = np.mean(data_a), np.mean(data_b) | |
| # squared standard error | |
| se_a, se_b = stats.tstd(data_a)**2.0/len(data_a), stats.tstd(data_b)**2.0/len(data_b) | |
| #v | |
| v_a, v_b = len(data_a)-1, len(data_b)-1 | |
| # assume unequal variance | |
| t_stat, p_2tail = ttest_ind(data_a, data_b, equal_var=False) | |
| # degree of freedom | |
| degreeF = ( (se_a+se_b)**2.0 )/ ( ((se_a**2.0)/v_a) + ((se_b**2.0)/v_b) ) | |
| # critical value | |
| cv = t.ppf(1.0-alpha, degreeF) | |
| # calculate the p-value,one sided | |
| p = (1.0-t.cdf(abs(t_stat), degreeF)) | |
| # h0: mean_a-mean_b<=0 ; h1: mean_a-mean_b>0 | |
| if t_stat > cv and mean_a > mean_b: | |
| ## trend | |
| return 1 | |
| else: | |
| ## not trend | |
| return 0 |
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