Using the sample data, perform one-way analysis of variance (ANOVA). Evaluate whether the average return ofat least oneof the industry-specific ETFs is significantly different. Use a 5% level of significance.

In your initial post, address the following items:

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.

In [1] : import pandas as pd # read data from etf_returns.csv. etf_returns_df = pd. read_csv('etf_returns.csv' ) # print etf returns data set. print (etf_returns_df) financial energy technology 5.5 5.2 7.3 7.1 7.4 8.2 6.9 6.6 7.1 5.1 5.7 7.6 4.6 5.6 8. 2 5.3 5.5 11.5 5.9 6.4 9.2 5.6 6. 1 9.5 5.5 5.2 7.3 7.1 7.4 8.2Click the block of code below and hit the Run button above. In [2]: H import scipy . stats as st # save return data for individual sectors for input to f_oneway method. etf_returns_financial = etf_returns_df[ ' financial' ] etf_returns_energy = etf_returns_df[ ' energy' ] etf_returns_technology = etf_returns_of [' technology' ] # print the outputs: the test statistic and the P-value. test_statistic, p_value = st. f_oneway(etf_returns_financial, etf_returns_energy, etf_returns_techno print("test statistic =", round(test_statistic, 2) ) print ( "P-value =", round(p_value, 4) ) test statistic = 55.07 P-value = 0.0HH