To be an outlier in the boxplot above, a score must be:

Question

BlackTom AI · Accepted Answer

First, let me restate what the boxplot indicates about the data: if Q1 is 60 and Q3 is 65, then the interquartile range (IQR) is 5. The conventional rule for identifying outliers in a boxplot uses 1.5 times the IQR.

- Lower outlier threshold = Q1 − 1.5 × IQR = 60 − 1.5 × 5 = 60 − 7.5 = 52.5.
- Upper outlier threshold = Q3 + 1.5 × IQR = 65 + 1.5 × 5 = 65 + 7.5 = 72.5.

Now, evaluating the option given:
- Option d: 'either less than 52.5 or greater than 72.5' accurately captures the two tails beyond these thresholds as outliers. This aligns with the standard outlier rule.

If there were alternative options, we would compare them similarly:
- Any statement claiming that values between 52.5 and 72.5 are outliers would be incorrect, since these fall within the typical non-outlier range.
- A claim that outliers are determined by the mean or standard deviation alone would be misleading here, as the boxplot rule relies on quartiles and IQR, not just the mean or spread.

In summary, based on the Q1, Q3, and IQR values visible in the boxplot, the boundary values for outliers are 52.5 and 72.5, making scores below 52.5 or above 72.5 outliers.

To be an outlier in the boxplot above, a score must be:

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To be an outlier in the boxplot above, a score must be:

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The five-number summary for the ages of the attendees at the Pawnee Harvest Festival is listed below. 1 20 29 38 80 I. There is at least one attendee whose age is a low potential outlier. II. There is at least one attendee whose age is a high potential outlier.

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Based on our in-class discussion, which of the following is not a potential cause of outliers?

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