1. Define the null and alternative hypothesis in mathematical terms and in words.
2. Report the level of significance.
3. Include the test statistic and the P-value. See Step 2 in the Python script.
4. Provide your conclusion and interpretation of the test. Should the null hypothesis be rejected? Why or why not?
5. Does a side-by-side boxplot of the 10-year returns of ETFs from the three sectors confirm your conclusion of the hypothesis test? Why or why not? See Step 3 in the Python script.

Education File 1 Help + Configure.... Jupyter Module Eight Discussion Last Checkpoint: 12/27/2019 (autosaved) Kernel Widgets Help Edit View C Markdown Step 1: Uploading the dataset WNPO 0 In [1]: import pandas as pd 1 3 #read data from etf_returns.csv. etf returns df pd.read_csv('etf_returns.csv) #print etf returns data set. print (etf_returns_df) 567 Insert The data for this discussion is included in a CSV file called etf_returns.csv. It contains ten-year returns of 30 ETFs for three sectors: financial, energy, and technology. The read_csv method in pandas can be used to upload the CSV. Click the block of code below and hit the Run button above. 88 9 Project Index (static) 10 1234567 Cell 11 N Run 5.5 7.1 6.9 5.1 4.6 5.3 5.9 5.6 5.5 7.1 6.9 5.1 4.6 5.3 5.9 5.6 4.7 6.4 Configure.... financial energy technology 5.2 7.4 6.6 5.7 5.6 5.5 6.4 6.1 5.2 7.4 6.6 5.7 5.6 5.5 6.4 5.1 4.4 6.6 7.3 8.2 7.1 7.6 8.2 11.5 9.2 9.5 7.3 8.2 7.1 7.6 8.2 11.5 9.2 9.5 6.2 7.4 RAMEN Trusted 2 Python 3 O