I would like to estimate the selling price of a used Toyota Camry. The independent variable is the "mileage" of the cars. The price is in dollars and the mileage is in miles. Multiple regression model is fitted to the data. Please use the partial outputs from a statistics software follow to answer the following questions (5-10):

What is the coefficient of correlation between the price and the mileage?

Question 5 options:

	-0.5423
	0.571
	0.5423
	-0.571

Question 6 (1 point)

On average, by how much will the price of the car increase (or decrease) if the car is used for 4000 more miles?

Question 6 options:

	increases by $599.149837
	increases by $599
	decreases by $599
	decreases by $599.149837

Question 7 (1 point)

What is the p-value associated with the test of hypothesis,

H0:1=0 vs. H1:1

Question 7 options:

	0.0028
	0.0055
	0.0110
	1.0055

Question 8 (1 point)

What is the conclusion of the above test of hypothesis? Use 5% level of significance.

Question 8 options:

	There is sufficient evidence to conclude that price decreases with the increase in miles.
	There is insufficient evidence to conclude that price increases with the increase in miles.
	There is some evidence to conclude that price decreases with the increase in miles.
	There is sufficient evidence to conclude that price increases with the increase in miles.

Question 9 (1 point)

What would be the 80% confidence interval for the true slope of the regression line?

Question 9 options:

	(0.0010, 0.0038)
	(-0.2136853, -0.08598871)
	(-0.2136853, 0.08598871)
	(0.08598871, 0.2136853)

Question 10 (1 point)

Predict the price of a car with 42000 miles in it.

Question 10 options:

	26625.23
	16628.12
	3224.23
	32157.23

I would like to estimate the selling price of a used Toyota Camry. The independent variable is the "mileage" of the cars. The price is in dollars and the mileage is in miles. Multiple regression model is fitted to the data. Please use the partial outputs from a statistics software follow to answer the following questions (5-10): Response Price Summary of Fit RSquare 0.326006 RSquare Adj 0.292306 Root Mean Square Error 4238.064 Mean of Response 14747.64 Observations (or Sum Wgts) 22 Analysis of Variance Sum of Source DF Squares Mean Square F Ratio Model 1 173753729 173753729 9.6738 Error 20 359223700 17961185 Prob > F C. Total 21 532977429 0.0055 Indicator Function Parameterization Term Estimate Std Error t Ratio Prob>|t| Intercept 22921.271 2778.937 8.25 <.0001 mileage>