Apply simple linear regression with the MPG as the dependent variable and weight as the independent variable. Based on this regression model, please answer questions

1.What is the proportion of variation that in MPG that is explained by the weight? (please enter as a number between 0-1 and not as a percent.)

2.What is the average change in the MPG for every additional 1000lbs of weight?

3.What would be the estimated MPG for a car weighing 3425lbs?(please round your answer to the closes 10th, i.e., the first decimal place)

4.Generate and examine a scatterplot of residuals by predicted MPG(residuals on the y-axis). Are there any patterns in the residuals that suggest violations of regression assumptions?(select all that apply)

a.No there are no patterns to suggest any assumptions may be violated

b.Yes there is a u-shape to the residuals indicating the assumption of linearity is likely violated

c.Yes the variation in residuals is greater (the residual values are more widely dispersed) the greater the predicted MPG indicating the assumption of homoskedasticity is likely violated

SUMMARY OUTPUT
Multiple R	0.8426809
R Square	0.7101111
Adjusted R Square	0.70935421
Standard Error	4.22500208
Observations	385
ANOVA
	df	SS	MS	F	Significance F
Regeression	1	16747.3984	16747.3984	938.19583	5.027E-105
Residual	383	6836.79611	17.8506426
Total	384	23584.1945
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	46.5027485	0.78295984	59.3935297	2.515E-195	44.9633107	48.0421863
Weight (1000s lbs)	-7.7305483	0.252385	-30.629983	5.027E-105	-8.2267819	-7.2343146