Here is the ordered data of body temperatures (in degrees Fahrenheit) for 65 healthy adult women:
96.4, 96.7, 96.8, 97.2, 97.2, 97.4, 97.6, 97.7, 97.7, 97.8, 97.8, 97.8, 97.9, 97.9, 97.9,
98.0, 98.0, 98.0, 98.0, 98.0, 98.1, 98.2, 98.2, 98.2, 98.2, 98.2, 98.2, 98.3, 98.3, 98.3,
98.4, 98.4, 98.4, 98.4, 98.4, 98.5, 98.6, 98.6, 98.6, 98.6, 98.7, 98.7, 98.7, 98.7, 98.7,
98.7, 98.8, 98.8, 98.8, 98.8, 98.8, 98.8, 98.8, 98.9, 99.0, 99.0, 99.1, 99.1, 99.2, 99.2,
99.3, 99.4, 99.9, 100.0, 100.8
Which value would need to be exceeded for a data point to be considered an outlier when using the 1.5 × IQR rule?

Question

Quizplus · Accepted Answer

To determine the value that would need to be exceeded for a data point to be considered an outlier using the 1.5 × IQR rule, first calculate the interquartile range (IQR). The IQR is the difference between the third quartile (Q3) and the first quartile (Q1). For 65 data points, Q1 is the 17th data point, and Q3 is the 49th data point when the data is ordered. Q1 = 97.9 and Q3 = 98.7. IQR = Q3 - Q1 = 98.7 - 97.9 = 0.8. The upper bound for outliers is Q3 + 1.5*IQR = 98.7 + 1.5*0.8 = 98.7 + 1.2 = 99.9. Therefore, a data point would be considered an outlier if it exceeds 99.9. The closest value listed that exceeds this threshold is 100.0.

Here Is the Ordered Data of Body Temperatures (In Degrees