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
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
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
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution!
Trending now
This is a popular solution!
Step by step
Solved in 3 steps with 3 images
Recommended textbooks for you
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman