12. | Expand Your Knowledge: Outliers Some data sets include values so high or so low that they seem to stand apart from the rest of the data. These data are called outliers. Outliers may represent data collection errors, data entry errors, or simply valid but unusual data values. It is important to identify outliers in the data set and examine them carefully to determine if they are in error. One way to detect outliers is to use a box-and-whisker plot. Data values that fall beyond the limits Lower limit: Q1 – 1.5 × (IQR) Upper limit: Q3 + 1.5 × (IQR) where IQR is the interquartile range, are suspected outliers. In the computer soft- ware package Minitab, values beyond these limits are plotted with asterisks (*). Students from a statistics class were asked to record their heights in inches. The heights (as recorded) were 65 72 68 64 60 55 73 71 52 63 61 74 69 67 74 50 4 75 67 62 66 80 64 65 (a) Make a box-and-whisker plot of the data. (b) Find the interquartile range (IQR). (c) Multiply the IQR by 1.5 and find the lower and upper limits. (d) Are there any data values below the lower limit? above the upper limit? List any suspected outliers. What might be some explanations for the outliers?

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ISBN:9781119256830
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12. | Expand Your Knowledge: Outliers Some data sets include values so high or so
low that they seem to stand apart from the rest of the data. These data are called
outliers. Outliers may represent data collection errors, data entry errors, or simply
valid but unusual data values. It is important to identify outliers in the data set and
examine them carefully to determine if they are in error. One way to detect outliers
is to use a box-and-whisker plot. Data values that fall beyond the limits
Lower limit: Q1 – 1.5 × (IQR)
Upper limit: Q3 + 1.5 × (IQR)
where IQR is the interquartile range, are suspected outliers. In the computer soft-
ware package Minitab, values beyond these limits are plotted with asterisks (*).
Students from a statistics class were asked to record their heights in inches.
The heights (as recorded) were
65
72
68
64
60
55
73
71
52
63
61
74
69
67
74
50
4
75
67
62
66
80
64
65
(a) Make a box-and-whisker plot of the data.
(b) Find the interquartile range (IQR).
(c) Multiply the IQR by 1.5 and find the lower and upper limits.
(d) Are there any data values below the lower limit? above the upper limit?
List any suspected outliers. What might be some explanations for the
outliers?
Transcribed Image Text:12. | Expand Your Knowledge: Outliers Some data sets include values so high or so low that they seem to stand apart from the rest of the data. These data are called outliers. Outliers may represent data collection errors, data entry errors, or simply valid but unusual data values. It is important to identify outliers in the data set and examine them carefully to determine if they are in error. One way to detect outliers is to use a box-and-whisker plot. Data values that fall beyond the limits Lower limit: Q1 – 1.5 × (IQR) Upper limit: Q3 + 1.5 × (IQR) where IQR is the interquartile range, are suspected outliers. In the computer soft- ware package Minitab, values beyond these limits are plotted with asterisks (*). Students from a statistics class were asked to record their heights in inches. The heights (as recorded) were 65 72 68 64 60 55 73 71 52 63 61 74 69 67 74 50 4 75 67 62 66 80 64 65 (a) Make a box-and-whisker plot of the data. (b) Find the interquartile range (IQR). (c) Multiply the IQR by 1.5 and find the lower and upper limits. (d) Are there any data values below the lower limit? above the upper limit? List any suspected outliers. What might be some explanations for the outliers?
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