The following table lists highway miles (in MPG) and weight (in pounds) for a variety of cars, foreign and domestic (same data as before). CAR HWY WEIGHT Chev. Cavalier 31 2795 Lincoln Cont. 24 3930 Mitsubishi Eclipse 33 3235 Olds. Aurora 26 3995 Pontiac Grand Am 30 3115 Chev. Corvette 28 3220 BMW 3-Series 27 3225 Ford Crown Victoria 24 3985 Hyundai Accent If we let x be the weight and y be the Highway MPG, and we used Excel to compute the equation of the Least- Square regression line (which comes out to be y = -0.00672'x + 51.1156) as well as the correlation coefficient (which comes out to be -0.89). Then you used that equation to predict that the Highway Miles for a car that weighs just 1000 pounds would be 44.4 mpg. 37 2290 O Ithink this prediction is accurate because the person used Excel for the computation. I think this prediction is not accurate since a car that weighs 1000 pounds is not part of the original data list. I think this prediction is not accurate because the person used the equation of a least-square regression line and the correlation coefficient is not equal to 1. O Ithink this prediction is accurate because the person used the equation of a least-square regression line and the correlation coefficient of -0.89 indicates a strong linear relation between x and y.
The following table lists highway miles (in MPG) and weight (in pounds) for a variety of cars, foreign and domestic (same data as before). CAR HWY WEIGHT Chev. Cavalier 31 2795 Lincoln Cont. 24 3930 Mitsubishi Eclipse 33 3235 Olds. Aurora 26 3995 Pontiac Grand Am 30 3115 Chev. Corvette 28 3220 BMW 3-Series 27 3225 Ford Crown Victoria 24 3985 Hyundai Accent If we let x be the weight and y be the Highway MPG, and we used Excel to compute the equation of the Least- Square regression line (which comes out to be y = -0.00672'x + 51.1156) as well as the correlation coefficient (which comes out to be -0.89). Then you used that equation to predict that the Highway Miles for a car that weighs just 1000 pounds would be 44.4 mpg. 37 2290 O Ithink this prediction is accurate because the person used Excel for the computation. I think this prediction is not accurate since a car that weighs 1000 pounds is not part of the original data list. I think this prediction is not accurate because the person used the equation of a least-square regression line and the correlation coefficient is not equal to 1. O Ithink this prediction is accurate because the person used the equation of a least-square regression line and the correlation coefficient of -0.89 indicates a strong linear relation between x and y.
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 following table lists highway miles (in MPG) and weight (in pounds) for a variety of cars, foreign and
domestic (same data as before).
CAR
HWY WEIGHT
Chev. Cavalier
31
2795
Lincoln Cont.
24
3930
Mitsubishi Eclipse 33
3235
Olds. Aurora
26
3995
Pontiac Grand Am 30
3115
Chev. Corvette
28
3220
BMW 3-Series
27
3225
Ford Crown Victoria 24
3985
Hyundai Accent
If we let x be the weight and y be the Highway MPG, and we used Excel to compute the equation of the Least-
37 2290
Square regression line (which comes out to be y = -0.00672'x + 51.1156) as well as the correlation coefficient
(which comes out to be -0.89). Then you used that equation to predict that the Highway Miles for a car that weighs
just 1000 pounds would be 44.4 mpg.
I think this prediction is accurate because the person used Excel for the computation.
O I think this prediction is not accurate since a car that weighs 1000 pounds is not part of the original data list.
O Ithink this prediction is not accurate because the person used the equation of a least-square regression line and
the correlation coefficient is not equal to 1.
O I think this prediction is accurate because the person used the equation of a least-square regression line and the
correlation coefficient of -0.89 indicates a strong linear relation between x and y.
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