Suppose a researcher is interested determining whether, on average, driving times on the major traffic routes are approximately the same or not. The following data are randomly collected from three major traffic routes. The entries in the table are driving times in minutes on the these routes.

Route 1	Route 2	Route 3
59	57	54
56	54	53
54	56	53
56	56	55
54	59	53
53	54	53
54	54	60
53	54	55
53	54	53
54	53	54
56		53
53		53
56		54
55		52
55		54
53
54
53
53
59

A One-Way ANOVA test was conducted at a 0.05 level of significance. The results are shown below.

Note that some values in the table might be in scientific notation. Say, 1.65E-07 means 1.65x10^-7or 0.000000165. ANOVA: Single Factor

SUMMARY
Groups	Count	Sum	Average	Variance
Route 1	20	1093	54.650	3.502632
Route 2	10	551	55.100	3.433333
Route 3	15	809	53.933	3.495238

ANOVA
Source of Variation	SS	df	MS	F	P-value	F crit
Between Groups	8.86111	2	4.43056	1.2712	0.291064	3.21994
Within Groups	146.38333	42	3.48532
Total	155.24444	44

Based on the Excel output, what conclusion is the most appropriate? choose one

• At the 0.05 level of significance, there is not sufficient sample evidence to support the claim that there is a difference in the average driving times. On average, the driving times are approximately the same.
• At the 0.05 level of significance, the sample data support the claim that there is a difference in the average driving times. On average, the driving times on the major traffic routes are not the same.
• For 0.05 level of significance the ANOVA test is unable to give a positive or a negative answer to the question whether different traffic routes affect the average driving times. The question needs futher investigation.
• For 0.05 level of significance the ANOVA test shows the sample means and the sample variances, but fails to answer the question whether different traffic routes affect the average driving times. Probably, the researcher should increase the sample sizes.
• None of the above