Consider the polynomial regression model of degree r, Y₁ = Po + B₁X₁ + B₂X² + ***+BX+μ₁- According to the null hypothesis that the regression is linear and the alternative that is a polynomial of degree r corresponds to: 0 vs. H₁: at least one ; #0, j= 2,...,. A. Ho: B₂=0, P3=0,..., B. H₁ : P₂ = 0, $3 = 0₁., 0 vs. H₁: all p;#0, j=2,...,r. OC. Ho : B₁ = 0 vs. H₁ : B₁ *0. D. Ho: p=0 vs. H₁ :p, *0.
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- Using least-squares regression, find a straight line that best fits the data in Table 1 below. xi yi 0.2 8.2 0.4 8.4 0.6 8.5 0.8 8.6 1.0 8.8 1.2 8.7 b. Using table 1 above, find the second-degree Lagrange interpolating polynomial that goes through the first three data points, and use it to find the interpolated value at xi = 0.48.3b. A linear regression yields R2 = 0. Does this imply that βˆ1 = 0?In an instrumental variable regression model with one regressor, Xi, andone instrument, Zi, the regression of Xi onto Zi has R2 = 0.1 and n = 50.Is Zi a strong instrument? Would your answer change if R2 = 0.1 and n = 150?
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