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a. Interpret the slope of this regression line if possible. If not, explain. b. Interpret the intercept of this regression line if possible. If not, explain. c. If the amount of power produced by the engine is 190 HP, what is the expected weight for this car? d. If the weight is 3,400 pounds for a car with engine power of 190 HP, what is the residual of weight? e. What percentage of the variability in weight can be explained by the regression line relationship with engine power? f. If we use weight to estimate the engine power in HP, what percentage of the variability in engine power can be explained by the regression line relationship with weight? g. Comparing your results from Q1.h and Q2.f, if you want to predict the engine power of a car, which variable you would prefer to choose? Retail price or weight? Explain.

a. Interpret the slope of this regression line if possible. If not, explain. b. Interpret the intercept of this regression line if possible. If not, explain. c. If the amount of power produced by the engine is 190 HP, what is the expected weight for this car? d. If the weight is 3,400 pounds for a car with engine power of 190 HP, what is the residual of weight? e. What percentage of the variability in weight can be explained by the regression line relationship with engine power? f. If we use weight to estimate the engine power in HP, what percentage of the variability in engine power can be explained by the regression line relationship with weight? g. Comparing your results from Q1.h and Q2.f, if you want to predict the engine power of a car, which variable you would prefer to choose? Retail price or weight? Explain.

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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2. Another variable the enthusiast recorded was the weight of the car in pounds. The regression with horsepower is given below. Simple linear regression results: Dependent Variable: Weight Independent Variable: HP Weight=2084.29 + 6.938 HP Sample size: 425 R (correlation coefficient) = 0.647 R-sq = 0.418 Estimate of error standard deviation: 581.01 Parameter estimates: Parameter Estimate Std. Err. Alternative Intercept 2084.29 90.14 +0 Slope 6.938 0.398 #0 DF T-Stat P-value 423 23.12 <0.0001 XXXX 423 XXXX a. Interpret the slope of this regression line if possible. If not, explain. b. Interpret the intercept of this regression line if possible. If not, explain. c. If the amount of power produced by the engine is 190 HP, what is the expected weight for this car? d. If the weight is 3,400 pounds for a car with engine power of 190 HP, what is the residual of weight? e. What percentage of the variability in weight can be explained by the regression line relationship with engine power? f. If we use weight to estimate the engine power in HP, what percentage of the variability in engine power can be explained by the regression line relationship with weight? g. Comparing your results from Q1.h and Q2.f, if you want to predict the engine power of a car, which variable you would prefer to choose? Retail price or weight? Explain.
Transcribed Image Text:2. Another variable the enthusiast recorded was the weight of the car in pounds. The regression with horsepower is given below. Simple linear regression results: Dependent Variable: Weight Independent Variable: HP Weight=2084.29 + 6.938 HP Sample size: 425 R (correlation coefficient) = 0.647 R-sq = 0.418 Estimate of error standard deviation: 581.01 Parameter estimates: Parameter Estimate Std. Err. Alternative Intercept 2084.29 90.14 +0 Slope 6.938 0.398 #0 DF T-Stat P-value 423 23.12 <0.0001 XXXX 423 XXXX a. Interpret the slope of this regression line if possible. If not, explain. b. Interpret the intercept of this regression line if possible. If not, explain. c. If the amount of power produced by the engine is 190 HP, what is the expected weight for this car? d. If the weight is 3,400 pounds for a car with engine power of 190 HP, what is the residual of weight? e. What percentage of the variability in weight can be explained by the regression line relationship with engine power? f. If we use weight to estimate the engine power in HP, what percentage of the variability in engine power can be explained by the regression line relationship with weight? g. Comparing your results from Q1.h and Q2.f, if you want to predict the engine power of a car, which variable you would prefer to choose? Retail price or weight? Explain.
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