To determine the average deviation for both datasets, we need to calculate the average of the "Distance from Mean" values for each table.
For Table 1:
- Distance from Mean values: 0, 4, 2, 1, 0, 1, 1, 1, 2
- Sum of distances = 0 + 4 + 2 + 1 + 0 + 1 + 1 + 1 + 2 = 12
- Number of data points = 9
- Average deviation = Sum of distances / Number of data points = 12 / 9 = 1.33 (approximately)
For Table 2:
- Distance from Mean values: 2, 1, 3, 3, 1, 1, 1, 1, 2
- Sum of distances = 2 + 1 + 3 + 3 + 1 + 1 + 1 + 1 + 2 = 15
- Number of data points = 9
- Average deviation = Sum of distances / Number of data points = 15 / 9 = 1.67 (approximately)
Now comparing the average deviations:
- Table 1 Average Deviation: 1.33
- Table 2 Average Deviation: 1.67
Since 1.67 (from Table 2) is greater than 1.33 (from Table 1), the correct response is:
The second table has a larger average deviation.