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In case of more than one variable, just picking of the rows and reshuffling the order will not make any difference as the data will remain same. So we reshuffle the y variable. Something what you have done, but I do not think we do not need double reshuffling of both x and y variables (as you have done).

In case of more than one variable, just picking of the rows and reshuffling the order will not make any difference as the data will remain same. So we reshuffle the y variable. Something what you have done, but I do not think we need double reshuffling of both x and y variables (as you have done).

"the regular bootstrap and the jackknife, estimate the variability of a statistic from the variability of that statistic between subsamples, rather than from parametric assumptions. For the more general jackknife, the delete-m observations jackknife, the bootstrap can be seen as a random approximation of it. Both yield similar numerical results, which is why each can be seen as approximation to the other." See this question on Bootstrap vs Jacknife.

See the question on permutation vs bootstrapping - "The permutation test is best for testing hypotheses and bootstrapping is best for estimating confidence intervals".

"the regular bootstrap and the jackknife, estimate the variability of a statistic from the variability of that statistic between subsamples, rather than from parametric assumptions. For the more general jackknife, the delete-m observations jackknife, the bootstrap can be seen as a random approximation of it. Both yield similar numerical results, which is why each can be seen as approximation to the other." See this question on Bootstrap vs Jacknife.

See the question on permutation vs bootstrapping - "The permutation test is best for testing hypotheses and bootstrapping is best for estimating confidence intervals".

Both MC and Permutation test are sometime collectively called randomization tests. The difference is in MC we sample the permutation samples, rather using all possible combinations [see] 21.

Both MC and Permutation test are sometime collectively called randomization tests. The difference is in MC we sample the permutation samples, rather using all possible combinations [see] 21.