The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 28 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 28 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? X Minitab output The regression equation is Highway = 51.0-0.00577 Weight Predictor SE Coef Constant 2.983 Weight -0.0057705 0.0007975 |S=2.16332 R-Sq = 63.8% R-Sq(adj) = 60.9% Predicted Values for New Observations New Obs 1 Coef 50.989 Fit 33.678 SE Fit 0.475 Weight 3000 T 17.65 - 7.39 Values of Predictors for New Observations New Obs 1 P 0.000 0.000 95% CI (32.665, 34.691) 95% Pl (29.085, 38.271)
The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 28 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 28 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? X Minitab output The regression equation is Highway = 51.0-0.00577 Weight Predictor SE Coef Constant 2.983 Weight -0.0057705 0.0007975 |S=2.16332 R-Sq = 63.8% R-Sq(adj) = 60.9% Predicted Values for New Observations New Obs 1 Coef 50.989 Fit 33.678 SE Fit 0.475 Weight 3000 T 17.65 - 7.39 Values of Predictors for New Observations New Obs 1 P 0.000 0.000 95% CI (32.665, 34.691) 95% Pl (29.085, 38.271)
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
64
Transcribed Image Text:The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 28 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 28 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?
X
Minitab output
The regression equation is
Highway = 51.0-0.00577 Weight
Predictor
SE Coef
Constant
2.983
Weight -0.0057705 0.0007975
|S=2.16332 R-Sq = 63.8% R-Sq(adj) = 60.9%
Predicted Values for New Observations
New
Obs
1
Coef
50.989
Fit
33.678
SE Fit
0.475
Weight
3000
T
17.65
- 7.39
Values of Predictors for New Observations
New
Obs
1
P
0.000
0.000
95% CI
(32.665, 34.691)
95% Pl
(29.085, 38.271)
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