The regression equation is Highway = 50.8 – 0.00568 Weight Predictor Coef SE Coef T P Constant 50.762 2.928 17.73 0.000 Weight - 0.0056831 0.0007883 - 7.13 0.000 S= 2.10096 R-Sq(adj) = 61.1% R- Sq = 64.2% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 959 1 28.030 0.498 (27.017, 29.043) (23.437 Values of Predictors for New Observations New Obs Weight 1 4000
The regression equation is Highway = 50.8 – 0.00568 Weight Predictor Coef SE Coef T P Constant 50.762 2.928 17.73 0.000 Weight - 0.0056831 0.0007883 - 7.13 0.000 S= 2.10096 R-Sq(adj) = 61.1% R- Sq = 64.2% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 959 1 28.030 0.498 (27.017, 29.043) (23.437 Values of Predictors for New Observations New Obs Weight 1 4000
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
1.
![Minitab output
The regression equation is
Highway = 50.8 – 0.00568 Weight
Predictor
Сoef
SE Coef
T P
Constant
50.762
2.928
17.73 0.000
Weight
- 0.0056831 0.0007883
- 7.13 0.000
S= 2.10096
R-Sq(adj) = 61.1%
R- Sq = 64.2%
Predicted Values for New Observations
New
Obs
Fit
SE Fit
95% CI
95% PI
1
28.030
0.498 (27.017, 29.043)
(23.437, 32.
Values of Predictors for New Observations
New
Obs
Weight
1
4000](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F4515bc91-0809-42a9-b9a4-ae265b7b62a4%2Fb156efcf-b7b9-42bf-bef6-2d4c20cc2e90%2Ftexvbeh_processed.png&w=3840&q=75)
Transcribed Image Text:Minitab output
The regression equation is
Highway = 50.8 – 0.00568 Weight
Predictor
Сoef
SE Coef
T P
Constant
50.762
2.928
17.73 0.000
Weight
- 0.0056831 0.0007883
- 7.13 0.000
S= 2.10096
R-Sq(adj) = 61.1%
R- Sq = 64.2%
Predicted Values for New Observations
New
Obs
Fit
SE Fit
95% CI
95% PI
1
28.030
0.498 (27.017, 29.043)
(23.437, 32.
Values of Predictors for New Observations
New
Obs
Weight
1
4000
![The Minitab output shown below was obtained by using paired data consisting of weights
(in Ib) of 26 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 26 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
No](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F4515bc91-0809-42a9-b9a4-ae265b7b62a4%2Fb156efcf-b7b9-42bf-bef6-2d4c20cc2e90%2Fhladdp_processed.png&w=3840&q=75)
Transcribed Image Text:The Minitab output shown below was obtained by using paired data consisting of weights
(in Ib) of 26 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 26 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
No
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