The theory of maximum-likelihood states that the estimated large-sample covariance for maximum-likelihood estimates is the inverse of the information matrix, where the elements of the information matrix are the negatives of the expected values of the second partial derivatives of the log-likelihood function evaluated at the maximum-likelihood estimates. Consider the linear regression model with normal errors. Find the information matrix and the covariance matrix of the maximum-likelihood estimates.