The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 27 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data. Minitab was also given a car weight of 3000 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 27 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? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Is there sufficient evidence to support a claim of linear correlation? O Yes O No Minitab output The regression equation is Highway = 50.3-0.00528 Weight Coef SE Coef T 2.919 17.56 Weight -0.0052842 0.0007857 -7.04 0.000 Predictor Constant P 0.000 50.309 S=2.27257 R-Sq=65.0% R-Sq(adj) = 62.1% Predicted Values for New Observations New Obs 1 Fit SE Fit 0.485 34.456 95% CI (33.451, 35.461) Values of Predictors for New Observations New Obs 1 Weight 3000 95% PI (29.864, 39.048) X
The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 27 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data. Minitab was also given a car weight of 3000 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 27 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? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Is there sufficient evidence to support a claim of linear correlation? O Yes O No Minitab output The regression equation is Highway = 50.3-0.00528 Weight Coef SE Coef T 2.919 17.56 Weight -0.0052842 0.0007857 -7.04 0.000 Predictor Constant P 0.000 50.309 S=2.27257 R-Sq=65.0% R-Sq(adj) = 62.1% Predicted Values for New Observations New Obs 1 Fit SE Fit 0.485 34.456 95% CI (33.451, 35.461) Values of Predictors for New Observations New Obs 1 Weight 3000 95% PI (29.864, 39.048) X
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 15PPS
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Question
Transcribed Image Text:The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 27 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 3000 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 27 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?
Click the icon to view the Minitab display.
The linear correlation coefficient is.
(Round to three decimal places as needed.)
Is there sufficient evidence to support a claim of linear correlation?
O Yes
O No
Minitab output
The regression equation is
Highway = 50.3-0.00528 Weight
T
Predictor
Coef SE Coef
Constant 50.309
2.919 17.56
Weight -0.0052842 0.0007857 - 7.04
|S=2.27257 R-Sq=65.0% R-Sq(adj) = 62.1%
C
Predicted Values for New Observations
New
Obs
1
Fit
34.456
Weight
3000
SE Fit
0.485
Values of Predictors for New Observations
New
Obs
1
Print
P
0.000
0.000
95% CI
(33.451, 35.461)
Done
95% PI
(29.864, 39.048)
X
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