Your friend is interested in generating a bootstrap sample from some original sample by sampling its observation indices. But you observe that he types sample(n,n,replace=TRUE) in R (where n is the original sample size). You argue that he should type sample(n,n,replace=FALSE) instead. Who is correct?     Both operations are acceptable and give the desired result. The replace input argument here is redundant and would only matter if we are carrying out cross-validation.     You are both wrong. You need to use more information in order to be able to generate a legitimate sample.     You are right. The sampling has to be done without replacement.     Your friend is right. Sampling without replacement will give identical estimates with respect to the original sample.

The module timeit (imported above) allows you to compute the time a function call takes. Verify your answer about the time complexity from Exercise I have attached by computing the run time for randomly chosen arc diagrams of suitable sizes and producing an appropriate plot of the average run times against the lengths of the matchings. Interpret your results.

Suppose we want to print the m largest values in an array N of n integers.  Of course, m is smaller than n. An approach is to perform quick sort to sort N and then use a loop to display the mlargest values at the end of N. What is the average-case running time in big-O notation of this approach?   What is the worst-case running time in big-O notation of this approach? You need to show your analysis to get credits.

Please send me answer within 10 mins I will upvote your answer for must. 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…