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) The outlier is a hybrid car that runs on gasoline and electric technology, but all other vehicles in the sample have engines that use gasoline only. Explain why it would be appropriate to remove the outlier from the data in this situation. a) The outlier is creating a curved least squares regression line. b) The outlier lies directly on the line, so the error residual (y − ŷ) is zero. c) The outlier does not lie directly on the line, but it is close. d) The outlier represents a different population of vehicles compared to the rest. Part 2) Remove the outlier from the sample data. Find the new correlation coefficient and coefficient of determination. (Round your answers to two decimal places.) correlation coefficient _______ coefficient of determination _ Part 3) Find the new best fit line. (Round your answers to four decimal places.) ŷ = 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

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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) The outlier is a hybrid car that runs on gasoline and electric technology, but all other vehicles in the sample have engines that use gasoline only. Explain why it would be appropriate to remove the outlier from the data in this situation.

a) The outlier is creating a curved least squares regression line.

b) The outlier lies directly on the line, so the error residual (y − ŷ) is zero.

c) The outlier does not lie directly on the line, but it is close.

d) The outlier represents a different population of vehicles compared to the rest.

Part 2) Remove the outlier from the sample data. Find the new correlation coefficient and coefficient of determination. (Round your answers to two decimal places.)

correlation coefficient _____________

coefficient of determination ___________

Part 3) Find the new best fit line. (Round your answers to four decimal places.)
ŷ = ______________ x + _______________

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