Recall our regular simple regression (SR) model is y t = 1 + 2 x

 Recall our “regular” simple regression (SR) model is y_t = β₁ + β₂x_t + e_t. But what if we knew that β₁ = 0 (i.e. that there is no intercept in our model)

(a) algebraically, what would our SR model look like in that case? (3 marks) What about graphically? (2 marks)

(b) using calculus and starting with the sum-of-squares function, derive the LS estimator b₂ given that you know β₁ = 0, similar to what we did in Lecture 3 when we derived our “regular” least-squares estimators. (10 marks)

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