A least-squares line is fit to a set of points. If the total sum of squares is Eo, - = 9615,and the error sum of squares is E0, - 9 = 1450, compute the coefficient ofdetermination r.

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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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 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…
We will fit a least-squares line y = Bo+ B1x to the data in the following table using x as the independent variable. 3 6 7 9 12 y 1 5 9 The mean values are: x=7 and y=5. Answer the following questions (select correct/closest choices). 1) Estimated value of the slope, B, is O 0.905 O 0.840 O 0.760 1.333 2) Estimated value of the intercept, Bo is O 1.333 -1.200 -0.320 O 3.200 3) The fitted y value when x=6 is O 3.816 O 4.664 4.240 3.604 4) The residual when x=6 is O 0.646 O 0.760 0.684 0.836 LO
Find the least squares approximating squares error for the points (1.1), (4.3). line and the least (2, 2), (3.2), and
A set of data is best fit by y = a, Using linear least squares, determine the coefficient: Select one: a. None b. Ex, /n c. E,y, In d. E,x y,/2,x Previous page Next page
The height (sidewalk to roof) of notable tall buildings in America is compared to the number of stories of the building (beginning at street level). Stories (x) Height (y) 59 1050 29 428 25 362 40 529 60 790 22 401 38 380 110 1454 100 1127 46 700 Calculate the least squares line. Put the equation in the form of: y = a + bx. (Round your answers to three decimal places.) ŷ = + X
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 315.81 28.31 11.24 0.002 Elevation -31.650 3.511 -8.79 0.003 S = 11.8603 R-Sq = 94.6% 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 : + %| (b) For each 1000-foot increase in elevation, how many fewer frost-free days…
b) The following incomplete y versus x data is given. 2 4. 7 6. ??? 15 5 11 2?? 32 The data is fit by quadratic spline interpolants given by ax -1, -2x? + 14x – 9, 1sxs2 f(x) = bx2 + cx + d, 25x2-303x + 928, 6SxS7 Determine the missing entries in the table and the coefficients, a, b. c, and d of these quadratic splines.
We use the form ý = 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. %3D A Minitab printout provides the following information. Predictor Сoef SE Coef P Constant 315.54 28.31 11.24 0.002 Elevation -28.950 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. = 315.54 X x (b) For each 1000-foot increase in elevation, how many fewer frost-free…
Find the mean of the regression squares 21 - a) 6,939 B) 0.162 NS) 6,973 D) 7,861 TO) 2,313
A researcher want to know if there is a relationship between sleep and anxiety. The researcher randomly selected n = 5 participants and asked each to rate their sleep quality and anxiety level on a scale of 1 to 5. The data are below: Sleep X Anxiety Y 2 2 3 3 2 2 4 2 5 2 Calculate the Sum of Squares for X (SSX)

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A least-squares line is fit to a set of points. If the total sum of squares is Eo, - = 9615, and the error sum of squares is E0, - 9 = 1450, compute the coefficient of determination r.