Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 31 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4000 lb to be used for predicting the highway fuel consumption amount. Use formation 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 31 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their way fuel consumption amounts? Click the icon to view the Minitab display. near correlation coefficient is nd to three decimal places as needed.) ere sufficient evidence to support a claim of linear correlation? Yes No Minitab output The regression equation is Highway = 50.8-0.00508 Weight Coef SE Coef T Р 50.772 2.793 17.73 0.000 Weight -0.0050805 0.0007931 -7.15 0.000 Predictor Constant S=2.20976 R-Sq=63.1% R-Sq(adj) = 60.5% Predicted Values for New Observations New Obs 1 Fit 30.450 SE Fit 0.472 Weight 4000 Values of Predictors for New Observations New Obs 1 95% CI (29.447, 31.453) Print Done 95% Pl (25.857, 35.043) - X
Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 31 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4000 lb to be used for predicting the highway fuel consumption amount. Use formation 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 31 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their way fuel consumption amounts? Click the icon to view the Minitab display. near correlation coefficient is nd to three decimal places as needed.) ere sufficient evidence to support a claim of linear correlation? Yes No Minitab output The regression equation is Highway = 50.8-0.00508 Weight Coef SE Coef T Р 50.772 2.793 17.73 0.000 Weight -0.0050805 0.0007931 -7.15 0.000 Predictor Constant S=2.20976 R-Sq=63.1% R-Sq(adj) = 60.5% Predicted Values for New Observations New Obs 1 Fit 30.450 SE Fit 0.472 Weight 4000 Values of Predictors for New Observations New Obs 1 95% CI (29.447, 31.453) Print Done 95% Pl (25.857, 35.043) - X
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 31 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4000 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 31 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?
Yes
O No
Minitab output
The regression equation is
Highway = 50.8 -0.00508 Weight
Predictor
Coef SE Coef
T
P
Constant 50.772
2.793 17.73 0.000
Weight -0.0050805 0.0007931 -7.15 0.000
S=2.20976 R-Sq=63.1% R-Sq(adj) = 60.5%
Predicted Values for New Observations
New
Obs
1
Fit
30.450
SE Fit
0.472
Weight
4000
Values of Predictors for New Observations
New
Obs
1
95% CI
(29.447, 31.453)
Print
Done
95% PI
(25.857, 35.043)
- X
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