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4.1 Answer each a. Suppose that a simple regression has quantities N = 20, Ey=7825.94, y = 19.21, and SSR=375.47, find R2. b. Suppose that a simple regression has quantities R² = 0.7911, SST = 725.94, and N = 20, find 6². C. Suppose that a simple regression has quantities (y₁ - y)² = 631.63 and 2 = 182.85, find R².

4.1 Answer each a. Suppose that a simple regression has quantities N = 20, Ey=7825.94, y = 19.21, and SSR=375.47, find R2. b. Suppose that a simple regression has quantities R² = 0.7911, SST = 725.94, and N = 20, find 6². C. Suppose that a simple regression has quantities (y₁ - y)² = 631.63 and 2 = 182.85, find R².

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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Answer 1a-c and 2a-c, thank you!
= exp(3.717 1.121 x ln(P)) 0.0139/2 4.7 Exercises 4.7.1 Problems 4.1 Answer each of the following: a. Suppose that a simple regression has quantities N= 20, SSR=375.47, find R2. a. All values of x were divided by 10 before estimation. b. All values of y were divided by 10 before estimation. c. All values of y and x were divided by 10 before estimation. on od sobteno. E a. the predicted value of y for xo = 4. b. the se(f) corresponding to part (a). c. a 95% prediction interval for y given xo = 4. TE 201.001 ai voda noia os bus 30 nowad nosism bab. Suppose that a simple regression has quantities R² = 0.7911, SST = 725.94, and N = 20, find ². BAT C. Suppose that a simple regression has quantities Σ(y, - y)² = 631.63 and 2 = 182.85, find R². 4.2 Consider the following estimated regression equation (standard errors in parentheses): y = 64.29 +0.99x R² = 0.379 (se) (2.42) (0.18) Rewrite the estimated equation, including coefficients, standard errors, and R2, that would result if y =7825.94, y = 19.21, and 4.3 We have five observations on x and y. They are x, = 3, 2, 1,-1,0 with corresponding y values y = 4,2,3,1,0. The fitted least squares line is ŷ; = 1.2 +0.8x;, the sum of squared least squares (y₁ - y)² = 10. Carry out this exercise with residuals is 2 t = 3.6, Σ. (x − x) = 10, and a hand calculator. Compute Model 2: d. a 99% prediction interval for y given xo = 4. e. a 95% prediction interval for y given x = x. Compare the width of this interval to the one computed in part (c). 4.4 The general manager of a large engineering firm wants to know whether the experience of techni- cal artists influences their work quality. A random sample of 50 artists is selected. Using years of work experience (EXPER) and a performance rating (RATING, on a 100-point scale), two models are estimated by least squares. The estimates and standard errors are as follows: Model 1: RATING= 64.289 +0.990EXPER N = 50 R² = 0.3793 (se) (2.422) (0.183) RATING= 39.464 + 15.312 In(EXPER) N = 46 R² = 0.6414 (se) (4.198) (1.727)
Transcribed Image Text:= exp(3.717 1.121 x ln(P)) 0.0139/2 4.7 Exercises 4.7.1 Problems 4.1 Answer each of the following: a. Suppose that a simple regression has quantities N= 20, SSR=375.47, find R2. a. All values of x were divided by 10 before estimation. b. All values of y were divided by 10 before estimation. c. All values of y and x were divided by 10 before estimation. on od sobteno. E a. the predicted value of y for xo = 4. b. the se(f) corresponding to part (a). c. a 95% prediction interval for y given xo = 4. TE 201.001 ai voda noia os bus 30 nowad nosism bab. Suppose that a simple regression has quantities R² = 0.7911, SST = 725.94, and N = 20, find ². BAT C. Suppose that a simple regression has quantities Σ(y, - y)² = 631.63 and 2 = 182.85, find R². 4.2 Consider the following estimated regression equation (standard errors in parentheses): y = 64.29 +0.99x R² = 0.379 (se) (2.42) (0.18) Rewrite the estimated equation, including coefficients, standard errors, and R2, that would result if y =7825.94, y = 19.21, and 4.3 We have five observations on x and y. They are x, = 3, 2, 1,-1,0 with corresponding y values y = 4,2,3,1,0. The fitted least squares line is ŷ; = 1.2 +0.8x;, the sum of squared least squares (y₁ - y)² = 10. Carry out this exercise with residuals is 2 t = 3.6, Σ. (x − x) = 10, and a hand calculator. Compute Model 2: d. a 99% prediction interval for y given xo = 4. e. a 95% prediction interval for y given x = x. Compare the width of this interval to the one computed in part (c). 4.4 The general manager of a large engineering firm wants to know whether the experience of techni- cal artists influences their work quality. A random sample of 50 artists is selected. Using years of work experience (EXPER) and a performance rating (RATING, on a 100-point scale), two models are estimated by least squares. The estimates and standard errors are as follows: Model 1: RATING= 64.289 +0.990EXPER N = 50 R² = 0.3793 (se) (2.422) (0.183) RATING= 39.464 + 15.312 In(EXPER) N = 46 R² = 0.6414 (se) (4.198) (1.727)
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