From this question, you will see that the second principal component has a strong relationship with 10-Year yield rates subtracted by 3-Month yield rates.

(1 point) Create a variable slope for the difference between column 10 yr and 3 mo of df.

(1 point) Create a figure that includes two line graphs for -pc2 and slope over date. (Notice that it is not pc2 but -pc2.)

(1 point) Print out the correlation coefficient between pc2 and slope to five decimal places.

(2 pint) Interpret the meaning the PC2. On the last line of your code, write your response as a commented test as follows.

Your code here # Your interpretation here

The graph for Part 2 must be as follows:

Hint: Replace the ????? part with your solution.

slope = df['?????']-df['?????'] fig = plt.figure(figsize=(15, 10)) plt.plot(?????, ?????, label='pc2') plt.plot(?????, ?????, label='10Y-3M (slope)') plt.legend(loc="upper right") plt.grid() plt.show() print('Correlation between -pc2 and slope',np.round(np.?????(-pc2, slope)[0,1],5)) # Your answer to Question 9-4 here