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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. Fuel Weight Efficiency 2685 24 2560 27 2660 29 2790 38 3000 25 3410 22

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. Fuel Weight Efficiency 2685 24 2560 27 2660 29 2790 38 3000 25 3410 22

MATLAB: An Introduction with Applications
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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Part (h) Identify any outliers, using either the graphical or numerical procedure demonstrated in the textbook. (Select all that apply.) O no outliers O (4710, 15) O (2685, 24) O (3700, 25) O (2790, 38) O (4060, 19) Part (i) 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. The outlier is creating a curved least squares regression line. The outlier does not lie directly on the line, but it is close. The outlier represents a different population of vehicles compared to the rest. The outlier lies directly on the line, so the error residual (y - ŷ) is zero. 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 Find the new best fit line. (Round your answers to four decimal places.) ŷ = X + Part (j) Compare the correlation coefficients and coefficients of determination before and after removing the outlier, and explain what these numbers indicate about how the model has changed. The new linear model is a better fit, because the new correlation coefficient is closer to zero. The first linear model is a better fit, because the first correlation coefficient is closer to zero. The new linear model is a better fit, because the new correlation coefficient is farther from zero. The first linear model is a better fit, because the first correlation coefficient is farther from zero. O O O O O O O O
Transcribed Image Text:Part (h) Identify any outliers, using either the graphical or numerical procedure demonstrated in the textbook. (Select all that apply.) O no outliers O (4710, 15) O (2685, 24) O (3700, 25) O (2790, 38) O (4060, 19) Part (i) 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. The outlier is creating a curved least squares regression line. The outlier does not lie directly on the line, but it is close. The outlier represents a different population of vehicles compared to the rest. The outlier lies directly on the line, so the error residual (y - ŷ) is zero. 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 Find the new best fit line. (Round your answers to four decimal places.) ŷ = X + Part (j) Compare the correlation coefficients and coefficients of determination before and after removing the outlier, and explain what these numbers indicate about how the model has changed. The new linear model is a better fit, because the new correlation coefficient is closer to zero. The first linear model is a better fit, because the first correlation coefficient is closer to zero. The new linear model is a better fit, because the new correlation coefficient is farther from zero. The first linear model is a better fit, because the first correlation coefficient is farther from zero. O O O O O O O O
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. Fuel Weight Efficiency 2685 24 2560 27 2660 29 2790 38 3000 25 3410 22 3640 20 3700 25 3880 21 3900 21 4060 19 4710 15 Part (a) + Part (b) O Part (c) + Part (d) O Part (e) 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 (f) O Part (g) For the vehicle that weighs 3000 pounds, find the residual (y - ŷ). (Round your answer to two decimal places.) Does the value predicted by the line underestimate or overestimate the observed data value? underestimate overestimate
Transcribed Image Text: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. Fuel Weight Efficiency 2685 24 2560 27 2660 29 2790 38 3000 25 3410 22 3640 20 3700 25 3880 21 3900 21 4060 19 4710 15 Part (a) + Part (b) O Part (c) + Part (d) O Part (e) 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 (f) O Part (g) For the vehicle that weighs 3000 pounds, find the residual (y - ŷ). (Round your answer to two decimal places.) Does the value predicted by the line underestimate or overestimate the observed data value? underestimate overestimate
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