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 - 0.0622*displacement S = 3.10179 R-squared = 66.9% Model 2: mpg vs horsepower The regression equation is mpg_city=32.4 - 0.0579*horsepower S = 3.30296 R-squared = 52.9% The variable horsepower is better because it has a higher residual standard error (S=3.30296) and a lower R-square (52.9%). The displacement variable is better because it has a higher R-square (66.9%). (C) The variable horsepower is better because it has a higher residual standard error (S=3.30296).
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 - 0.0622*displacement S = 3.10179 R-squared = 66.9% Model 2: mpg vs horsepower The regression equation is mpg_city=32.4 - 0.0579*horsepower S = 3.30296 R-squared = 52.9% The variable horsepower is better because it has a higher residual standard error (S=3.30296) and a lower R-square (52.9%). The displacement variable is better because it has a higher R-square (66.9%). (C) The variable horsepower is better because it has a higher residual standard error (S=3.30296).
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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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 - 0.0622*displacement
S = 3.10179
R-squared = 66.9%
Model 2: mpg vs horsepower
The regression equation is
mpg_city=32.4 - 0.0579*horsepower
S = 3.30296
R-squared = 52.9%
- The variable horsepower is better because it has a higher residual standard error (S=3.30296) and a lower R-square (52.9%).
- The displacement variable is better because it has a higher R-square (66.9%).
(C) The variable horsepower is better because it has a higher residual standard error (S=3.30296).
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