Assume that we are given sample points {(x1, y1),...,(xn, yn)}. In the class, we derived the slope 1 and intercept 0 minimizing Pn i=1(yi1xi0)2. Now, assume that instead of mean squared error, we are interested in minimizing total squares. Specifically, we want to find a line such that the sum of the squared distance of data points (xi, yi) to that line is minimized. The dierence between ordinary regression and the minimum total square is shown in the figure below