Chrome File Edit View History Bookmarks Profiles Tab Window Help [III B A Q Fri Sep 30 8:24 PM ed (25) Data X Homewo X hw05.pd x Media G X Lec 09 - X Salva 41 X hw05.pd X M Inbox (7, x hw05.pd x C drive.google.com/file/d/15DbOKR9bB2BJRnHxOF1gcVadgFYDu2XI/view 1. (7 points) In lecture, we spent a great deal of time talking about simple linear regression, which you also saw in Data 8. To briefly summarize, the simple linear regression model DCIM assumes that given a single observation x, our predicted response for this observation is y = 00 + 01x. (Note: In this problem we write (60, 01) instead of (a, b) to more closely mirror the multiple linear regression model notation.) In Lecture 9 we saw that the 60 = 60 and 01 = 01 that minimize the average L2 loss for the simple linear regression model are: 00 = y -Q15 A =ry ra de Captura de 7.04 p.m. Pantalla...6.13 p.m. Or, rearranging terms, our predictions y are: a x - I HTTP HTTP y = y troy ox Slides (html) Exam Prep 04 (a) (2 points) As we saw in lecture, a residual e; is defined to be the difference between a true response y; and predicted response yi. Specifically, ei = yi - yi. Note that there are n data points, and each data point is denoted by (xi, yi). a to_o HTTP Show, using the equation for y above, that the average prediction is also the average lestions (1).scm value of the response, i.e. " Zi=1yi = n Li=1yi. HW 5 (Due 2/23 a HTTP HTTP lides (html) 3 Slides (pdf) a HTTP HTTP des (html) 2 Solutions (b) (3 points) Show that (x, y) is on the simple linear regression line. SEP 30 A O