Question 43. Consider the following code chunk and output and note that there are two warnings. import pandas as pd import statsmodels.formula. api as sm import matplotlib.pyplot as plt X1 =...

Extracted text: Question 43. Consider the following code chunk and output and note that there are two warnings. import pandas as pd import statsmodels.formula. api as sm import matplotlib.pyplot as plt X1 = np.random.normal (0, 1, 100) X2 = X1 + 3 Y = X1 + X2 + np.random.normal (0, 1, 100) df - pd. DataFrame ({"Y": Y, "X1": X1, "X2": X2}) linmodel = sm.ols (formula = "Y X1 + X2", data = df).fit () linmodel.summary () Output: Intercept -0.0272 X1 1.1074 X2 1.0257 Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The smallest eigenvalue is 3.27e-31. This might indicate that there are strong multicollinearity problems or that the design matrix is singular. Which of the following statements are correct? a) Perfectly correlated regressors X1 and X2 are used. b) Either X1 or X2 should be excluded, as the second regressor does not add any information to the model. c) The second warning indicates that the model fit to the data is perfect.