In your jupyter notebook import the following library according to this code
	import numpy as np
	import pandas as pd
	from matplotlib import pyplot
	import statsmodels.api as sm
	import statsmodels.formula.api as smf
	Place the csv file''Health_sector_returns_20170531-20220627.csv'' in your working directory and read it into a dataframe named returns. Use this file to answer questions in this activity
27	The number of rows and columns in returns is
	# rows	# columns
1	1000	10
2	10	1000
3	1300	13
4	13	1300
5	None of the above
	Define a dataframe X = returns[['SPY_ret']] and a panda series Y=returns['MRK_ret'] . Add a constant column if 1 to X, and fit an OLS regression model and output its summary. (Hint: review how we did it in the previous video and the part of the notebook implemented for LLY_ret
28	According to your regression output, the R_squared of the regression is
1	0.2234
2	0.3245
3	0.0845
4	0.2745
5	None of the above
29	The corelation between MRK_ret and SPY_ret is
1	0.432
2	0.5239
3	-0.5235
4	0.6734
5	None of the above