Y₁ =B₁ + B₁X₁ + e, (2) In which , and B, are OLS estimates of b, and b₁, respectively. n Σx, Yi (1) Prove the OLS estimate of B₁ is B₁= n [x² i=1 and the OLS estimate of B is B-Y-B‚¸Ñ n in which x₁ = X₁-X, X=-¶X₁ ni=1 n y₁=Y-Y, Y=LĖX ΣΥ ni=1
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- The least squares regression line for a set of data is calculated to be y = 24.8 + 3.41x. (a) One of the points in the data set is (4, 37). Calculate the predicted value. (b) For the point in part (a), calculate the residual.Suppose the PRF of two-variable regression is represented in the equation (1) Y₁ =b₁ + b₁X₁ + u₁, u~i.i.d N(0,0²) (1) We use Ordinary Least Square (OLS) method to estimate the coefficients in (1), The OLS estimated SRF is represented in the equation (2) Y₁ =B₁ + B₁X₁ + e, (2) In which , and B, are OLS estimates of b, and b, respectively. (2) Prove Σe, = 0 (3) Prove Sex = 0The estimate of b in the regression equation y=a+bx+error by the method of least square is
- Find the equation y = Bo + B1x of the least-squares line that best fits the given data points. (0,1), (1,1), (2,5), (3,5) The line is y =+ (Ox. (Type integers or decimals.)Find the least-squares regression line ŷ bo+ b₁z through the points For what value of is y = 0? H= (-2,0), (1, 6), (4, 14), (8, 19), (10, 27).Find the least-squares regression line ý = b + b₁ through the points (-2,0), (0,9), (5, 15), (8,20), (12,24). For what value of x is y = 0
- Find the equation y = Bo + B,x of the least-squares line that best fits the given data points. (- 1,0), (0,3), (1,5). (2,7) The line is y =+ (Dx. (Type integers or decimals.)Find the least-squares regression line ŷ = ba + b₁ through the points (−2, 2), (3, 6), (5, 14), (8, 19), (10, 27), and then use it to find point estimates corresponding to x = 1 and x = = 7. For x = 1, y = For x = 7, y =Find the least-squares regression line ŷ = bo + b1r through the points %3D (-1,2), (2, 6), (5, 13), (7, 20), (10, 23), and then use it to find point estimates y corresponding to x = 3 and x = 6. For = 3, y = %3D For I = 6, y = %3D
- Find the least square regression line for the data points : (1,1), (2,3). (4,5).29. Below is the result of running linear regression in R. The least-squares regression equation is Call: Lm(formula = variable_C ~ variable_W) Coefficients: (Intercept) -5.5162 variable_W 0.2666 A. variable_W -5.5162 + 0.2666 * variable C B. variable C = -5.5162 + 0.2666 * variable W C. variable W = 0.2666 – 5.5162 * variable C D. variable_C = 0.2666 – 5.5162 * variable_W B. bO. None of the above. MacBook Air DD 80 888 000 F3 F4 F6 F7 F8 F9 F5 # $ & 8An experiment was performed on a certain metal to determine if the strength is a function of heating time. Results based on 10 metal sheets are given below. ∑ X = 30 ∑ X 2 = 104 ∑ Y = 40 ∑ Y 2 = 178 ∑ XY = 134 Using the simple linear regression model, find the estimated y-intercept and slope and write the equation of the least squares regression line.