A consumer advocacy group recorded several variables on 140 models of cars. The resulting information was used to produce two models for predicting miles per gallon in the city (mpg_city), one based on the engine displacement (in cubic inches) and a second one based the power of the engine (in horsepower). Model 1: mpg vs engine displacement The regression equation is mpg_city=33.8 - 0.063*displacement S = 3.26269 R-squared = 66.7% Model 2: mpg vs horsepower The regression equation is mpg_city=32.2 - 0.0577*horsepower S = 4.06386 R-squared = 49.7% The displacement variable is better because it has a higher R-square (66.7%).The variable horsepower is better because it has a higher residual standard error (S=4.06386). The variable horsepower is better because it has a higher residual standard error (S=4.06386) and a lower R-square (49.7%).The displacement variable is better because it has a lower estimate for the residual standard error (S=3.26269).The displacement variable is better because it has a lower estimate for the residual standard error (S=3.26269) and a higher R-square (66.7%).
A consumer advocacy group recorded several variables on 140 models of cars. The resulting information was used to produce two models for predicting miles per gallon in the city (mpg_city), one based on the engine displacement (in cubic inches) and a second one based the power of the engine (in horsepower). Model 1: mpg vs engine displacement The regression equation is mpg_city=33.8 - 0.063*displacement S = 3.26269 R-squared = 66.7% Model 2: mpg vs horsepower The regression equation is mpg_city=32.2 - 0.0577*horsepower S = 4.06386 R-squared = 49.7% The displacement variable is better because it has a higher R-square (66.7%).The variable horsepower is better because it has a higher residual standard error (S=4.06386). The variable horsepower is better because it has a higher residual standard error (S=4.06386) and a lower R-square (49.7%).The displacement variable is better because it has a lower estimate for the residual standard error (S=3.26269).The displacement variable is better because it has a lower estimate for the residual standard error (S=3.26269) and a higher R-square (66.7%).
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2AGP
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A consumer advocacy group recorded several variables on 140 models of cars.
The resulting information was used to produce two models for predicting miles per gallon in the city (mpg_city), one based on the engine displacement (in cubic inches) and a second one based the power of the engine (in horsepower).
Model 1: mpg vs engine displacement
The regression equation is
mpg_city=33.8 - 0.063*displacement
S = 3.26269
R-squared = 66.7%
Model 2: mpg vs horsepower
The regression equation is
mpg_city=32.2 - 0.0577*horsepower
S = 4.06386
R-squared = 49.7%
The displacement variable is better because it has a higher R-square (66.7%).The variable horsepower is better because it has a higher residual standard error (S=4.06386). The variable horsepower is better because it has a higher residual standard error (S=4.06386) and a lower R-square (49.7%).The displacement variable is better because it has a lower estimate for the residual standard error (S=3.26269).The displacement variable is better because it has a lower estimate for the residual standard error (S=3.26269) and a higher R-square (66.7%).
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