2. Calculate the following quantities using least-squares (give coefficients to three decimal places):(a) The least-squares solution to the inconsistent system x+3y=9, 3r+ y = 5, x+y=2.(b) The least-squares line y = mx +b approximating the points {(4,7), (11, 21), (15,29), (19,35), (30,49)}.(c) The least-squares quadratic y = ar²+bx+c approximating the points {(-2,22), (-1,11), (0,4), (1,3), (2, 13)}.

(a) To find the least squares solution to the inconsistent system. x+3y=93x+y=5x+y=2 (b) To find the…

Answered: 2. Calculate the following quantities…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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We use the form ŷ = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from a report showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in a state. A Minitab printout provides the following information. Predictor Coef SE Coef T P Constant 315.27 28.31 11.24 0.002 Elevation -31.812 3.511 -8.79 0.003 S = 11.8603 R-Sq = 96.8% Notice that "Elevation" is listed under "Predictor." This means that elevation is the explanatory variable x. Its coefficient is the slope b. "Constant" refers to a in the equation ŷ = a + bx. (a) Use the printout to write the least-squares equation. ŷ = + x (b) For each 1000-foot increase in elevation,…
We use the form ŷ = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from a report showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in a state. A Minitab printout provides the following information. Predictor Coef SE Coef T P Constant 316.62 28.31 11.24 0.002 Elevation -30.516 3.511 -8.79 0.003 S = 11.8603 R-Sq = 96.2% The printout gives the value of the coefficient of determination r2. What is the value of r? Be sure to give the correct sign for r based on the sign of b. (Round your answer to four decimal places.) What percentage of the variation in y can be explained by the corresponding variation in x and the least-squares…
9. Find the equation y = ax² + bx + c of the least-square parabola that best fits the given data points (-1,0), (0,3), (1,3), (2,5), and (3,9).
H1.
Again Find the parabola that gives the best least squares approximation to the points. Please solve this problem as fast as possible please. Do not pay attention to the selected option, I am just trying myself at linear algebra :)
3 Find the least squares cubic equation that best fits the points: (-2,-2), (-1, 1), (0, 1), (1, 2), (2, 2).
In a study to predict temperature from air pressure in a piston-cylinder device, 19 measurements were made of temperature in °F (y) and air pressure in psi (x). Three models were fit: the linear model y = Bo + B,x + ɛ, the quadratic model y = Bo + Bix + B2x² + ɛ, and the cubic model y = Bo + B,x + B2x? + Bx3 + E. The residuals and fitted values for each model are presented in the following table. Plot the residuals versus the fitted values for each model. For each model, state whether the model is appropriate, and explain. %3D Quadratic Model Fit Linear Model Cubic Model Residual Fit Residual Residual Fit -56.2 125.6 11.2 58.2 3.3 66.1 -34.0 153.1 -7.4 126.5 -6.7 125.7 8.4 179.8 4.9 183.4 9.2 179.0 21.4 207.2 -3.6 232.2 0.9 227.8 28.6 234.7 -8.2 271.5 -5.9 269.2 46.9 260.9 8.1 299.7 7.3 300.5 47.2 288.1 15.7 319.6 12.1 323.2 8.5 314.4 -7.0 329.8 -11.7 334.6 -7.1 342.0 4.0 330.9 1.0 333.9 -47.1 139.3 -1.3 93.6 -4.1 96.4 -1.6 166.2 9.1 155.5 12.1 152.5 38.0 220.9 5.9 253.0 9.5 249.4…
Find the equation of the line .that best fits, in the least squares sense, the data in the following table: xy 55 6 10 8 13 9 20 12 28 Form the system for m and b by substituting the points in the model, e.g. substituting the first point leaves the equation : 5 m + b = 5 Report the value of m:

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