60. Given n data points ($1, y1 ) , . . ., (In, Un), the linear least-squares fit is the linear function f (a) = mx + b that minimizes the sum of the squares (Figure 26): E(m, b) = (y; - f (x;))2 Show that the minimum value of E occurs for m and b satisfying the two equations m ton = 1= 1 n m M 2 02 + 6 = xjy; j=1 j=1 ( Xn , yn) y = mx + b (x2, )2) (X1, )1) ! (x; , y;) Rogawski et al., Calculus, 4e, 2019 W. H. Freeman and Company FIGURE 26 The linear least-squares fit minimizes the sum of the squares of the vertical distances from the data points to the line