Find the standard error of the estimate and the coefficient of determination for the least-squares regression line y = bo + b₁ through the points (-3, 2), (1, 8), (5, 14), (7, 20), (9, 23).
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- Find the least squares regression line for the data points. (Let x be the independent variable and y be the dependent variable.)(−1, 0), (1, −1), (3, −3)Use the general equation for the least square regression line to show that this line always passes through the point (x,y) * bars above the x and y.That is, set x=x(with a bar above the x) and show that the line predicts that y=y (with a bar above the y).Find the least squares regression line for the points. (0, 5), (3, 3), (5, 1), (7, -4), (9, -5) 1.) y =
- For a least squares regression line, the sum of the residuals is __________. always negative sometimes positive and sometimes negative always zero always positiveFind the coefficients for the least-squares regression line y^=b0+b1x through the points (−2,0),(2,6),(4,14),(9,20),(9,25) b0 =. b1 =Find the least-squares regression line y^=b0+b1x through the points (−1,1),(1,8),(5,14),(8,20),(11,27) and then use it to find point estimates ?̂y^ corresponding to x=1 and x=6. For x=1, y^ = For x=6, y^ =
- Use the least squares regression to fit a straight line to: Along with the slope and intercept, compute the standard error of the estimate and the correlation coefficient. Plot the data and the regression lineFind the least squares regression line for the given points. (−4,−1),(−2,0),(2,4),(4,5)Find the least squares regression line.(0, 0), (1, 1), (2, 4)
- A researcher at a large company has collected data on the beginning salary and current salary of 48 randomly selected employees. The least-squares regression equation for predicting their current salary from theirbeginning salary isY = -2500 + 2.1X Where Y is the current salary, and X is the beginning salary.Jay started working for the company earning $ 30,000. He currently earns $60,000. What is the residual for Jay (assuming no extrapolation error)? Please show calculationsFind the least squares regression line and calculate S, the sum of the squared errors. Use the regression capabilities of a graphing utility to verify your results.Suppose that the regression model is defined as: y=B1x+u Use the least squares method to estimate the slope of the regression line. Define the objective function that will be used in the estimation. Lastly, enumerate the steps in deriving the estimator of B1