The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by Consumer Reports (Consumer Reports website). The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ŷ = 24.7173 + 0.3028¤ , where a = price ($) and y = overall score. Brand Price ($) Score Bose 180 75 Scullcandy 150 72 Koss 95 61 Phillips/O'Neill 60 56 Denon 60 40 JVC 55 26 a. Compute SST, SSR, and SSE (to 3 decimals). SST = 1792 SSR = 1297.61: SSE = 494.386 b. Compute the coefficient of determination r (to 3 decimals). p2 = .724 Comment on the goodness of fit. Hint: If r? is greater than 0.70, the estimated regression equation provides a good fit. The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line c. What is the value of the sample correlation coefficient (to 3 decimals)? rxy = .366
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by Consumer Reports (Consumer Reports website). The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ŷ = 24.7173 + 0.3028¤ , where a = price ($) and y = overall score. Brand Price ($) Score Bose 180 75 Scullcandy 150 72 Koss 95 61 Phillips/O'Neill 60 56 Denon 60 40 JVC 55 26 a. Compute SST, SSR, and SSE (to 3 decimals). SST = 1792 SSR = 1297.61: SSE = 494.386 b. Compute the coefficient of determination r (to 3 decimals). p2 = .724 Comment on the goodness of fit. Hint: If r? is greater than 0.70, the estimated regression equation provides a good fit. The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line c. What is the value of the sample correlation coefficient (to 3 decimals)? rxy = .366
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
I need help just figuring out what part of the excel sheet tells me the answer for Rxy so i can be sure I know what to do. I know the rest of it! Thanks!

Transcribed Image Text:The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by Consumer Reports (Consumer Reports website). The
overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for
these data is ŷ = 24.7173 + 0.3028x , where x = price ($) and y = overall score.
Brand
Price ($)
Score
Bose
180
75
Scullcandy
150
72
Koss
95
61
Phillips/O'Neill
60
56
Denon
60
40
JVC
55
26
a. Compute SST, SSR, and SSE (to 3 decimals).
SST =
1792
SSR =
1297.61:
SSE =
494.386
b. Compute the coefficient of determination r2 (to 3 decimals).
p2 =
.724
Comment on the goodness of fit. Hint: If r2 is greater than 0.70, the estimated regression equation provides a good fit.
The least squares line provided
a good fit as a large
proportion of the variability in y has been explained by the least squares line.
c. What is the value of the sample correlation coefficient (to 3 decimals)?
Txy
.366

Transcribed Image Text:SUMMARY OUTPUT
Regression Statistics
Multiple R 0.811731
R Square 0.658907
Adjusted R 0.545209
Standard E 12.21357
Observatic
5
ANOVA
df
SS
MS
ignificance F
Regressior
1 864.4861 864.4861 5.795257 0.095245
Residual
3 447.5139 149.1713
Total
4
1312
Coefficientsandard Erre
t Stat
P-value Lower 95%Upper 95%ower 95.0%pper 95.0%
Intercept 20.29521 13.87502 1.462715 0.239727 -23.8613 64.45173 -23.8613 64.45173
180 0.365533 0.151842 2.407334 0.095245 -0.11769 0.848761 -0.11769 0.848761
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