http://www.ck12.org Chapter 2. Visualizations of Data
Setting a window is not as important for a box plot, so we will use the calculator’s ability to automatically scale a
window to our data by pressing[ZOOM]and select number 9 (ZoomStat).
While box plots give us a nice summary of the important features of a distribution, we lose the ability to identify
individual points. The left whisker is elongated, but if we did not have the data, we would not know if all the points
in that section of the data were spread out, or if it were just the result of the one outlier. It is more typical to use a
modified box plot. This box plot will show an outlier as a single, disconnected point and will stop the whisker at the
previous point. Go back and change your plot to the first box plot option, which is the modified box plot, and press
then graph it.
Notice that without the outlier, the distribution is really roughly symmetric.
This data set had one obvious outlier, but when is a point far enough away to be called an outlier? We need a standard
accepted practice fordefiningan outlier in a box plot. This rather arbitrary definition is that any point that is more
than 1.5 times the Interquartile Range will be considered an outlier. Because the IQR is the same as the length of the
box, any point that is more than 1 and a half box lengths from either quartile is plotted as an outlier.
A common misconception of students is that you stop the whisker at this boundary line. In fact, the last point on the
whisker that is not an outlier is where the whisker stops.