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 the outliers 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 software 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 as follows. 66 73 67 63 59 56 74 72 51 62 60 75 70 66 75 49 5 74 66 61 65 81 65 64 (a) Make a box-and-whisker plot of the data. The box-and-whisker plot has a horizontal axis numbered from 0 to 90. The box-and-whisker is also horizontal. The left whisker is approximately 5, the left edge of the box is approximately 60.5, the line inside the box is approximately 65.5, the right edge of the box is approximately 72.5, and the right whisker is approximately 81. The box-and-whisker plot has a horizontal axis numbered from 0 to 90. The box-and-whisker is also horizontal. The left whisker is approximately 5, the left edge of the box is approximately 45.5, the line inside the box is approximately 5