The Minitab output shown below was obtained by using paired data consisting of weights​ (in lb) of 32 cars and their highway fuel consumption amounts​ (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 5000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient.​ (Be careful to correctly identify the sign of the correlation​ coefficient.) Given that there are 32 pairs of​ data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption​ amounts?     View the Minitab display. ( Minitab output attached)

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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The Minitab output shown below was obtained by using paired data consisting of weights​ (in lb) of 32 cars and their highway fuel consumption amounts​ (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 5000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient.​ (Be careful to correctly identify the sign of the correlation​ coefficient.) Given that there are 32 pairs of​ data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption​ amounts?
 
 
View the Minitab display.
( Minitab output attached) 
 
The linear correlation coefficient is___________.
​(Round to three decimal places as​ needed.)
 
Is there sufficient evidence to support a claim of linear​ correlation?
 
A. No
B. Yes
 
Minitab output
The regression equation is
Highway = 50.7 - 0.00553 Weight
Predictor
Constant
Weight
Coef
SE Coef
2.774
0.0007834
P
50.672
17.89
0.000
-0.0055327
7.33
0.000
S-2.11135
R-Sq 66 6%
R-Sq(adj)= 64.3%
Predicted Values for New Observations
New
Obs
Fit
SE Fit
95% CI
95% PI
(18.411, 27.607)
11
23.009
0.529
(22.007, 24.011)
Values of Predictors for New Observations
New
Obs
Weight
5000
Transcribed Image Text:Minitab output The regression equation is Highway = 50.7 - 0.00553 Weight Predictor Constant Weight Coef SE Coef 2.774 0.0007834 P 50.672 17.89 0.000 -0.0055327 7.33 0.000 S-2.11135 R-Sq 66 6% R-Sq(adj)= 64.3% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI (18.411, 27.607) 11 23.009 0.529 (22.007, 24.011) Values of Predictors for New Observations New Obs Weight 5000
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