x=[427.63 519.72 494.38 424.96 505.94 527.90 365.77 495.38 547.73 423.65 429.28 596.78 392.58 390.14 384.70 375.69 432.92 488.43 397.33 558.75 503.72 406.12 444.66 563.87 372.24 422.71 435.40 589.94 560.96 561.18 547.23 411.88 559.77 429.69 501.97 492.57 484.28 468.08 550.47 454.36 464.88 552.52 447.98 582.22 515.48 418.38 481.44 398.57 573.89 582.77 557.97 541.89 377.00 429.79 446.62 470.44 537.61 576.90 524.07 541.58] y=[0.06248 0.47896 0.29003 0.30006 0.53384 0.19718 0.31117 0.21319 0.68306 0.09837 0.39216 0.43184 0.18348 0.18735 0.10409 0.32311 0.34046 0.34810 0.28723 0.43491 0.52523 0.14492 0.41073 0.50077 0.22843 0.28671 0.26240 0.61549 0.42420 0.52507 0.60639 0.34340 0.32693 0.41590 0.71702 0.40851 0.24640 0.15213 0.46033 0.52980 0.35733 0.34464 0.27471 0.75605 0.60971 0.00301 0.34440 0.48404 0.45184 0.53569 0.61713 0.48550 0.26580 0.35604 0.14637 0.31730 0.29517 0.41179 0.49588 0.55522]
(a) Fit the linear regression of Y on X using Matlab. What is the estimated intercept term ?
(b) What is the estimated slope of the fitted line, ?
(c) What is the estimated residual variance