Python으로 배우는 선형 모델 입문
Jason Vestuto
Data Scientist
질문: 효과가 관계에 의한 것인가, 무작위 우연에 의한 것인가?
답: 귀무 가설을 확인하십시오.
# Group into early and late times group_short = sample_distances[times < 5] group_long = sample_distances[times > 5]
# Resample distributions resample_short = np.random.choice(group_short, size=500, replace=True) resample_long = np.random.choice(group_long, size=500, replace=True)
# Test Statistic test_statistic = resample_long - resample_short
# Effect size as mean of test statistic distribution effect_size = np.mean(test_statistic)
# Concatenate and Shuffle shuffle_bucket = np.concatenate((group_short, group_long)) np.random.shuffle(shuffle_bucket)
# Split in the middle slice_index = len(shuffle_bucket)//2 shuffled_half1 = shuffle_bucket[0:slice_index] shuffled_half2 = shuffle_bucket[slice_index+1:]
# Resample shuffled populations shuffled_sample1 = np.random.choice(shuffled_half1, size=500, replace=True) shuffled_sample2 = np.random.choice(shuffled_half2, size=500, replace=True)
# Recompute effect size shuffled_test_statistic = shuffled_sample2 - shuffled_sample1 effect_size = np.mean(shuffled_test_statistic)
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