Consider a linear model of the form 3}}; m was; which is a linear model, but with the intercept forced to zero. This occurs in applications where we want to force the predicted value 3} = 0 when cc = O. For example, if we are modeling 3; = output power of a motor vs. .11: = the input power, we would expect :13 = U > y = 0. Note: for symbolic input questions, click on the ? next to the input box to see what variables you can use in your answer. Part 1: Cost function Given data (athyg), write a cost function representing the residual sum of squares (RSS) between 9; and the predicted value 3},- as a function of w. RSS(w) = 2:1 y*123 0 Part 2: Regression coefficients Find the w that minimizes the RSS. First, compute the derivate and set it to zero: ORSS(w) a(w) = Em x*y 0 Then, simplify, and find the optimal value of w. Express the answer as a ratio of two sums: w = Zi=1 11 Ei-1 23