To determine the aspect of the data causing inconsistencies in each data set, we should analyze how the values deviate from the "correct" value:
Data Set I: [tex]\(190, 315, 198, 194, 200, 195, 540, 201, 197, 193\)[/tex] with a correct value of [tex]\(195\)[/tex].
- In this data set, most of the values are clustered around [tex]\(195\)[/tex], but there is one value ([tex]\(540\)[/tex]) which is significantly larger than the others. This extreme value is known as an outlier and can greatly impact the overall statistics of the data set.
- Therefore, the inconsistency here is caused by Outliers.
Data Set II: [tex]\(18, 51, 52, 50, 50, 49, 51, 53\)[/tex] with a correct value of [tex]\(65\)[/tex].
- The values in this data set are all relatively close to each other, but they are all significantly lower than the correct value of [tex]\(65\)[/tex]. This indicates that the measurements are consistently off the mark.
- Therefore, the inconsistency here is due to a Lack of accuracy.
Data Set III: [tex]\(9, 13, 14, 19, 11, 20, 19, 12\)[/tex] with a correct value of [tex]\(15\)[/tex].
- The values in this data set are spread out around the correct value of [tex]\(15\)[/tex], with some being significantly lower and some being significantly higher. This spread suggests that measurements vary widely.
- Therefore, the inconsistency here is due to a Lack of precision.
Thus, the evidence of inconsistencies in the data sets can be summarized as follows:
- Data Set I: Outliers
- Data Set II: Lack of accuracy
- Data Set III: Lack of precision
In conclusion:
- Data Set I is flagging Outliers as the cause of inconsistency.
- Data Set II is flagging a Lack of accuracy as the cause of inconsistency.
- Data Set III is flagging a Lack of precision as the cause of inconsistency.
