DATAfile: Sales A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 80 Years of Annual Sales Salesperson Experience ($1,000s) 1 80 2 3 97 3 4 92 4 4 102 103 6 111 7 10 119 8 10 123 9 11 117 10 13 136 (a) Compute SST, SSR, and SSE. SST = 2502 X SSR = 2272 SSE = 230 X (b) Compute the coefficient of determination r. (Round your answer to three decimal places.) ,2= 908 x Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) 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. The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did not provide 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? (Round your answer to three decimal places.) .953X

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DATAfile: Sales
A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ŷ = 80 + 4x.
Years of
Annual Sales
Salesperson
Experience
($1,000s)
1
80
2
3
97
3
4
92
4
4
102
103
6
111
7
10
119
8
10
123
9
11
117
10
13
136
(a) Compute SST, SSR, and SSE.
SST =
2502 X
SSR = 2272
SSE = 230 X
(b) Compute the coefficient of determination r. (Round your answer to three decimal places.)
,2= 908 x
Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.)
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.
The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares
line.
The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line.
The least squares line did not provide 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? (Round your answer to three decimal places.)
.953X
Transcribed Image Text:DATAfile: Sales A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ŷ = 80 + 4x. Years of Annual Sales Salesperson Experience ($1,000s) 1 80 2 3 97 3 4 92 4 4 102 103 6 111 7 10 119 8 10 123 9 11 117 10 13 136 (a) Compute SST, SSR, and SSE. SST = 2502 X SSR = 2272 SSE = 230 X (b) Compute the coefficient of determination r. (Round your answer to three decimal places.) ,2= 908 x Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) 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. The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did not provide 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? (Round your answer to three decimal places.) .953X
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