12 Consider the problem described at the end of Section 2-6, running a regression and only estimating an intercept. (i) Given a sample {y; i = 1,2, ..., n}, let ß, be the solution to min (y; – bo). Show that B, = y, that is, the sample average minimizes the sum of squared residuals. (Hint: You may use one-variable calculus or you can show the result directly by adding and subtract- ing y inside the squared residual and then doing a little algebra.) (ii) Define residuals ü; = y; – y. Argue that these residuals always sum to zero.

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12 Consider the problem described at the end of Section 2-6, running a regression and only estimating an
intercept.
(i) Given a sample {y; i = 1,2, ..., n}, let ß, be the solution to
min (y; – bo).
Show that B, = y, that is, the sample average minimizes the sum of squared residuals. (Hint:
You may use one-variable calculus or you can show the result directly by adding and subtract-
ing y inside the squared residual and then doing a little algebra.)
(ii) Define residuals ü; = y; – y. Argue that these residuals always sum to zero.
Transcribed Image Text:12 Consider the problem described at the end of Section 2-6, running a regression and only estimating an intercept. (i) Given a sample {y; i = 1,2, ..., n}, let ß, be the solution to min (y; – bo). Show that B, = y, that is, the sample average minimizes the sum of squared residuals. (Hint: You may use one-variable calculus or you can show the result directly by adding and subtract- ing y inside the squared residual and then doing a little algebra.) (ii) Define residuals ü; = y; – y. Argue that these residuals always sum to zero.
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