Consider the following regression output. Sale price = b0+b1(Odometer)+e

SUMMARY OUTPUT
Regression Statistics		Descriptive Statistics
Multiple R	0.4028		Xbar	67541.92
R Square	0.1623		Sx	29865.08
Adjusted R Square	0.1157		Sy	1341.13
Standard Error	1261.1428
Observations	20
ANOVA
	df	SS	MS	F	Significance F
Regression	1	5545197.38	5545197.38	3.49	0.08
Residual	18	28628662.41	1590481.25
Total	19	34173859.79
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	80458.942	712.511	112.923	0.000	78962.011	81955.873
Odometer reading	-0.018	0.010	-1.867	0.078	-0.038	0.002

1. Test at both the 5% and 10% levels if the coefficients of this regression are significantly different from zero.
   1. What does it mean in this case if you fail to reject?
   2. Explain briefly why it is important to state your significance levelbefore your read a output table such as this.
2. Compute the average sale price of the sample.
3. Suppose a given vehicle has an odometer reading of 100,000km,
   1. What would be the predicted sale price of this vehicle?