Please complete all parts of the question below thoroughly and show work! Much appreciated!!

Question 7 a) Solve the Simple Linear Regression least-squares minimization problem Not yet answered (Bo, B1) = arg min(do,1)ER2 En=(@0 + 01Xn - yn)2 (*) Points out of 1.00 as in Equation (11) on Slide 14 of the course slides for the parameters 3, and 31 , respectively, for the particular case of N=3 and the Flag question three specific data points (0,6), (1,0), and (0,0). Give the two numerical values that you compute for 3, and 3, (and state them in this order with 3, first and then , second). Explain the method and show the computations you used to get your answer (or at least enough of them to make clear how you arrived at your answer). You are free to solve for the two numerical values , and 3, by any suitable method you would like, using if desired any results or theorems presented in, for example, the course slides (but please make clear that you are using any such results if you do). b) Suppose that, rather than using the least-squares minimization problem (*) as stated above in part (a) of this quiz question to compute Bo and 31, one attempted to use the following optimization (minimization) problem instead: arg min(20,1)ER2 2n=1 0 + @1Xn - ynl , (**) where | . | denotes the absolute value. What is a key difference -- or difficulty - one would encounter if one attempted to solve the problem (**) for 3, and 3, via the same, traditional methods (see for example the discussion on Course Slide 14) we have been using and discussing in this course to solve the problem (*) above? In what way does this difference present a difficulty or potential obstacle to the solution of (**) via these same methods