Using test data on 43 vehicles, an analyst fitted a regression to predict CityMPG (miles per gallon in city driving) using as predictors Length (length of car in inches), Width (width of car in inches), and Weight (weight of car in pounds). R20.682 Adjusted R20.658n43R0.826k3Standard error2.558Dependent variableCityMPG ANOVA tableSourceSSdfMSFp-valueRegression547.37223182.457427.908.35E-10Residual255.0929396.5408 802.465142 Regression outputconfidence intervalVIFvariablesCoefficientsStandard errort statisticp-ValueLower 95%Upper 95%Intercept39.44928.16784.8300.000022.928355.9701 Length (in)−0.00160.0454−0.0350.9725−0.09340.09022.669Width (in)−0.04630.1373−0.3370.7379−0.32390.23142.552Weight (lbs)−0.00430.0008−5.1660.0000−0.0060−0.00262.836 (a) Referring to the Fstatistic and its p-value, what do you conclude about the overall fit of this model?

It is needed to test the overall fit of the model.

Answered: Using test data on 43 vehicles, an…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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Using test data on 43 vehicles, an analyst fitted a regression to predict CityMPG (miles per gallon in city driving) using as predictors Length (length of car in inches), Width (width of car in inches), and Weight (weight of car in pounds).

R²	0.682
Adjusted R²	0.658	n	43
R	0.826	k	3
Standard error	2.558	Dependent variable	CityMPG

Source	SS	df	MS	F	p-value
ANOVA table
Regression	547.3722	3	182.4574	27.90	8.35E-10
Residual	255.0929	39	6.5408
	802.4651	42

variables	Coefficients	Standard error	t statistic	p-Value	Lower 95%	Upper 95%
Regression output	confidence interval	VIF
Intercept	39.4492	8.1678	4.830	0.0000	22.9283	55.9701
Length (in)	−0.0016	0.0454	−0.035	0.9725	−0.0934	0.0902	2.669
Width (in)	−0.0463	0.1373	−0.337	0.7379	−0.3239	0.2314	2.552
Weight (lbs)	−0.0043	0.0008	−5.166	0.0000	−0.0060	−0.0026	2.836