1. 2. Derive the least squares estimator of ẞo for the regression model Y₁ = Po +εi- points Given the data (X) and (Y), we will assume that a regression model Y₁ = B₁Xi + &; is appropriate with normally distributed independent error terms and variance σ² = 16. i 1 2 3 4 Xi 7 12 25 30 Yi 128 213 446 540 (a) State the likelihood function for the four Y observations. (b) Evaluate the likelihood function for ẞ₁ = 17 and B₁ = 19. For which of these is the likelihood function the largest? (c) Find the maximum likelihood estimates for ẞ₁ and ẞo and using Sxx, Sxy, and the means for Xi and Yi, respectively, as demonstrated in the instructional videos.

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