I need some assistance with these 2 questions

1. Use your model to estimate the price of a car that has 100,000 miles. Is this prediction reasonable? Why or why not?
2. Provide a brief conclusion or discussion of the results that includes one of the following:
   • potential problems or sources of bias
   • are the results what you expected?
   • are there other variables that may also be useful to predict the price of a car

These the data I have so far

Simple linear regression results:

Dependent Variable: Price Independent Variable: Mileage Price = 41116.768 - 0.17675013 Mileage Sample size: 30 R (correlation coefficient) = -0.46576164 R-sq = 0.2169339 Estimate of error standard deviation: 9248.6584

Parameter estimates:

Parameter	Estimate	Std. Err.	Alternative	DF	T-Stat	P-value
Intercept	41116.768	2661.5978	0	28	15.448152
Slope	-0.17675013	0.06346236	0	28	-2.7851175	0.0095

Analysis of variance table for regression model:

Source	DF	SS	MS	F-stat	P-value
Model	1	6.6350549e8	6.6350549e8	7.7568794	0.0095
Error	28	2.3950551e9	85537682
Total	29	3.0585606e9

Price	Mileage
28900	28330
41000	21650
41499	24376
38999	4644
25383	24053
43499	4863
34980	6432
61829	10643
39499	82140
28691	40210
39502	59130
28998	30000
42094	10762
30749	41148
41000	21650
24795	91785
26500	19971
45590	4220
43343	8324
30975	24909
19500	101449
25995	51195
28500	39942
35999	34883
22997	23005
33499	11342
27499	33706
33975	17546
31313	86891
64499	13372