Find the least-squares quadratic fit tho the following data:t -5.1 -3.1-1.51.7 2.1 3.1y-1.94.15.1 -4.5 -1.9 5.0 5.9

t y -5.1 -1.9 -3.1 4.1 -1.5 5.1 0 -4.5 1.7 -1.9 2.1 5.0 3.1 5.9 quadratic equation…

Answered: Find the least-squares quadratic fit…

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…
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. Constant 317.97 28.31 11.24 0.002 Elevation -28.572 3.511 -8.79 0.003 S = 11.8603 R-Sq 94.2% %3D 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. %3D (b) For each 1000-foot increase in elevation, how many fewer frost-free days are…
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 Сoef SE Coef T Constant 317.43 28.31 11.24 0.002 Elevation -31.272 3.511 -8.79 0.003 S = 11.8603 R-Sq = 96.2% 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 = 317.43 -31.272 (b) For each 1000-foot increase in elevation, how many fewer…
2. The Mach number of a moving object is the ratio of its speedto the speed of sound. The following table shows the speeds of a jet aircraft, in terms of Mach numbers, and the time tafter it starts to accelerate. Find the least- squares line of s as a function of t. t(min) 0.00 s(Mach Number) 0.88 0.60 0.97 1.20 1.03 1.80 1.11 2.40 1.19 3.00 1.25
When a stone is dropped in a pond, ripples are formed and travel in concentric circles away from where the stone was dropped. The equation of the least-squares regression line is log(Area) = 0.490 + 2.004 log(Time). WWhat is the predicted area of the circle, in cm2, 4 seconds after the stone is dropped? O 49.72 cm2 O 199.43 cm2 O 311.89 cm? O 1854.10 cm2
It is thought that basketball teams that make too many fouls in a game tend to lose the game even if they otherwise play well. Let x be the number of fouls more than (i.e., over and above) the opposing team. Let y be the percentage of times the team with the larger number of fouls wins the game. 4 y 48 42 33 26 A USE SALT Complete parts (a) through (e), given Ex = 15, Ey = 149, Ex² = 77, Ey² = 5833, Exy = 489, and r= -0.9106. (a) Draw a scatter diagram displaying the data. 55 Graph Layers After you add an object to the graph you 50 can use Graph Layers to view and edit its properties. Fill 45 40 35 No Solution 30 25 20 O Help WebAssign. Graphing Tool ( b) Verify the given sums Σχ, Σγ, Σχ, Σy, Σχy, and the value of the sample correlation coefficient r. (Round your value forr to four decimal places.) Σχ Ey = Ex? = Ey? = Exy = r =
а. Complete the table. (or just write answers) Xi Yi 2 2 3 4 Totals E x; = E yi = Exf = Σχy Find SSxy, SSXX B1. x, y, and fo- Write the equation of the least squares line. b. C. d) What will be y if x=10

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Find the least-squares quadratic fit tho the following data: t-5.1 -3.1 -1.5 1.7 2.1 3.1 -1.9 4.1 5.1 -4.5 -1.9 5.0 5.9