We are interested in exploring the relationship between the weight of a vehicle and its fuel efficiency (gasoline mileage). The data in the table show the weights, in pounds, and fuel efficiency, measured in miles per gallon, for a sample of 12 vehicles. Weight Fuel Efficiency 2710 24 2560 28 2630 29 2740 38 3000 26 3410 25 3640 21 3700 27 3880 21 3900 20 4060 20 4710 17 Part 1) What percent of the variation in fuel efficiency is explained by the variation in the weight of the vehicles, using the regression line? (Round your answer to the nearest whole number.) _______________% Part 2) For the vehicle that weighs 3000 pounds, find the residual  (y − ŷ).(Round your answer to two decimal places.) ______________ Part 3) Identify any outliers, using either the graphical or numerical procedure demonstrated in the textbook. (Select all that apply.) a) (4710, 17) b) (2740, 38) c) (2710, 24) d) (3700, 27) e) (4060, 20) f) no outliers

Linear Algebra: A Modern Introduction
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ISBN:9781285463247
Author:David Poole
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Chapter4: Eigenvalues And Eigenvectors
Section4.6: Applications And The Perron-frobenius Theorem
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We are interested in exploring the relationship between the weight of a vehicle and its fuel efficiency (gasoline mileage). The data in the table show the weights, in pounds, and fuel efficiency, measured in miles per gallon, for a sample of 12 vehicles.

Weight Fuel
Efficiency
2710 24
2560 28
2630 29
2740 38
3000 26
3410 25
3640 21
3700 27
3880 21
3900 20
4060 20
4710 17

Part 1) What percent of the variation in fuel efficiency is explained by the variation in the weight of the vehicles, using the regression line? (Round your answer to the nearest whole number.)

_______________%

Part 2) For the vehicle that weighs 3000 pounds, find the residual  (y − ŷ).(Round your answer to two decimal places.)

______________

Part 3) Identify any outliers, using either the graphical or numerical procedure demonstrated in the textbook. (Select all that apply.)

a) (4710, 17)
b) (2740, 38)
c) (2710, 24)
d) (3700, 27)
e) (4060, 20)
f) no outliers
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