Llnear regressmn resuluals 1. (7 points) In lecture7 we spent a great deal of time talking about simple linear regression7 which you also saw in Data 8. To briey summarize, the simple linear regression model assumes that given a single observation 3:: our predicted response for this observation is 3} = (90 + 913:. (Note: In this problem we write (60, 61) instead of ((1,1)) to more closely mirror the multiple linear regression model notation.) In Lecture 9 we saw that the ('90 2 {9A0 and (91 = 91 that minimize the average L2 loss for the simple linear regression model are: (c) (2 points) Show that the residuals are uncorrelated with the predictor variable, that is 1 n i__ i__ i: (e e) (a: 3:) : 05 n i=1 are or _ 1 n _ 2 _ r n . _ 2 where e E 22:] EI and (IE n 21:1(61 e) . You may assume that at least one res1dual is not exactly zero