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1) Take a bit of time to learn how to find the Spearman’s correlation here scipy.stats.spearmanr — SciPy v1.9.1 Manual. Run some of the sample code shown. You don’t need to turn in anything for this question. Warning: There is a mistake in the documentation. The documentation says: “Unlike the Pearson correlation, the Spearman correlation does not assume that both datasets are normally distributed.” This is wrong. The datasets need NOT be normally distributed to compute the regular correlation!

2) Create a scatterplot of the IBM returns versus the MSFT returns. Turn in this plot. Comment on the relationship between the two returns. (For example, is there a relationship?)

3) Do you think there could be some outliers? Answer this question using the scatterplot. Answers may vary.

4) Report the point estimate of the correlation coefficient between IBM and MSFT. Round to 3 decimal places. 5) Report the point estimate of the Spearman’s correlation coefficient between IBM and MSFT. Round to 3 decimal places.

6) You’ll notice that the Spearman’s correlation coefficient is larger. Explain why this could be the case. 7) Use bootstrapping to obtain a 95% confidence for the correlation coefficient. Set b = 1000. Use a seed number of 999. Turn in the following: a. The histogram of the bootstrapped correlation values b. The standard error for the correlation coefficient c. The 95% CI for the correlation coefficient Round the CI limits to 3 decimal places. 8) Use bootstrapping to obtain a 95% confidence for the Spearman’s correlation coefficient. Again, set b = 1000 and, use a seed number of 999. Warning: Blindly copying and pasting the code will not work; I hope you have been reading the code comments. Turn in the following: a. The histogram of the bootstrapped Spearman’s correlation values b. The standard error for the Spearman’s correlation coefficient c. The 95% CI for the Spearman’s correlation coefficient Round the CI limits to 3 decimal places. 9) Compare and contrast the 2 intervals you obtained. For example, you can compare their widths. Answers may vary.

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