Python: Run a paired bootstrap test to compare the means e1 and e2. I was able to make a bootstrap test to find whether or not two datasets have significantly different means but I'm not sure what to add to make it a paired bootstrap. The paired bootstrap should look exactly like this but with one added component. What would need to be added to make it a paired bootstrap? e1_2 = e1_sub + e1_e2_com e2_2 = e2_sub + e1_e2_com # create array to hold bootstrap mean differences nbootstraps = 10000 bs_mean_diffs = np.zeros(nbootstraps) # take bootstrap samples many times for ii in range(nbootstraps): # choose which indices will be used from e1_2 and e2_2 inds1 = np.random.randint(0,len(e1)) inds2 = np.random.randint(0,len(e2)) # create your bootstrap samples bs_e1 = e1_2[inds1] bs_e2 = e2_2[inds2] # measure their difference and store it bs_mean_diffs[ii] = bs_e1.mean()- bs_e2.mean() # take the absolute value of each bootstrap difference, and find the fraction that are # larger than the absolute mean difference between d1 and d2. this is the bootstrap p-value bs_pval = (np.abs(bs_mean_diffs) > np.abs(np.mean(e1) - np.mean(e2))).mean() print('bootstrap p-value:', bs_pval) Output: bootstrap p-value :0.9422
Python: Run a paired bootstrap test to compare the means e1 and e2. I was able to make a bootstrap test to find whether or not two datasets have significantly different means but I'm not sure what to add to make it a paired bootstrap. The paired bootstrap should look exactly like this but with one added component. What would need to be added to make it a paired bootstrap? e1_2 = e1_sub + e1_e2_com e2_2 = e2_sub + e1_e2_com # create array to hold bootstrap mean differences nbootstraps = 10000 bs_mean_diffs = np.zeros(nbootstraps) # take bootstrap samples many times for ii in range(nbootstraps): # choose which indices will be used from e1_2 and e2_2 inds1 = np.random.randint(0,len(e1)) inds2 = np.random.randint(0,len(e2)) # create your bootstrap samples bs_e1 = e1_2[inds1] bs_e2 = e2_2[inds2] # measure their difference and store it bs_mean_diffs[ii] = bs_e1.mean()- bs_e2.mean() # take the absolute value of each bootstrap difference, and find the fraction that are # larger than the absolute mean difference between d1 and d2. this is the bootstrap p-value bs_pval = (np.abs(bs_mean_diffs) > np.abs(np.mean(e1) - np.mean(e2))).mean() print('bootstrap p-value:', bs_pval) Output: bootstrap p-value :0.9422
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Python: Run a paired bootstrap test to compare the means e1 and e2. I was able to make a bootstrap test to find whether or not two datasets have significantly different means but I'm not sure what to add to make it a paired bootstrap. The paired bootstrap should look exactly like this but with one added component. What would need to be added to make it a paired bootstrap?
e1_2 = e1_sub + e1_e2_com
e2_2 = e2_sub + e1_e2_com
# create array to hold bootstrap mean differences
nbootstraps = 10000
bs_mean_diffs = np.zeros(nbootstraps)
# take bootstrap samples many times
for ii in range(nbootstraps):
# choose which indices will be used from e1_2 and e2_2
inds1 = np.random.randint(0,len(e1))
inds2 = np.random.randint(0,len(e2))
# create your bootstrap samples
bs_e1 = e1_2[inds1]
bs_e2 = e2_2[inds2]
# measure their difference and store it
bs_mean_diffs[ii] = bs_e1.mean()- bs_e2.mean()
# take the absolute value of each bootstrap difference, and find the fraction that are
# larger than the absolute mean difference between d1 and d2. this is the bootstrap p-value
bs_pval = (np.abs(bs_mean_diffs) > np.abs(np.mean(e1) - np.mean(e2))).mean()
print('bootstrap p-value:', bs_pval)
Output: bootstrap p-value :0.9422
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