Please note that this must be done with MATLAB.

5.8. Here are 25 observations, , taken at equally spaced values of t. t 1:25 y 5.0291 6.5099 5.3666 4.1272 4.2948 6.1261 12.5140 10.0502 9.1614 7.5677 7.2920 10.0357 11.0708 13.4045 12.8415 11.9666 11.0765 11.7774 14.5701 17.0440 17.0398 15.9069 15.4850 15.5112 17.6572] (a) Fit the data with a straight line, y(t)-BB2t, and plot the residuals, y(t)-yk You should observe that one of the data points has a much larger residual than the others. This is probably an outlier. (b) Discard the outlier, and fit the data again by a straight line. Plot the residuals again. Do you see any pattern in the residuals? (c) Fit the data, with the outlier excluded, by a model of the form y(t)-A + At + As sin t. d) Evaluate the third fit on a finer grid over the interval [0, 26]. Plot the fitted curve, using line style '-', together with the data, using line style 'o Include the outlier, using a different marker, '*' 5.8. Here are 25 observations, , taken at equally spaced values of t. t 1:25 y 5.0291 6.5099 5.3666 4.1272 4.2948 6.1261 12.5140 10.0502 9.1614 7.5677 7.2920 10.0357 11.0708 13.4045 12.8415 11.9666 11.0765 11.7774 14.5701 17.0440 17.0398 15.9069 15.4850 15.5112 17.6572] (a) Fit the data with a straight line, y(t)-BB2t, and plot the residuals, y(t)-yk You should observe that one of the data points has a much larger residual than the others. This is probably an outlier. (b) Discard the outlier, and fit the data again by a straight line. Plot the residuals again. Do you see any pattern in the residuals? (c) Fit the data, with the outlier excluded, by a model of the form y(t)-A + At + As sin t. d) Evaluate the third fit on a finer grid over the interval [0, 26]. Plot the fitted curve, using line style '-', together with the data, using line style 'o Include the outlier, using a different marker, '*