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3. Mark the location of each number in the five-number summary just
above the number line (for a horizontal boxplot) or just to the right of
the number line (for a vertical boxplot).
4. Draw a box around the marks for the 25th percentile and the 75th
percentile.
5. Draw a line in the box where the median is located.
6. Determine whether or not outliers are present.
To make this determination, calculate the IQR (by subtracting Q – Q );
then multiply by 1.5. Add this amount to the value of Q and subtract this
amount from Q . This gives you a wider boundary around the median
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than the box does. Any data points that fall outside this boundary are
determined to be outliers.
7. If there are no outliers (according to your results of Step 6), draw lines
from the upper and lower edges of the box out to the minimum and
maximum values in the data set.
Part II: Number-Crunching Basics 3 3 1
8. If there are outliers (according to your results of Step 6), indicate their
location on the boxplot with * signs. Instead of drawing a line from
the edge of the box all the way to the most extreme outlier, stop the
line at the last data value that isn’t an outlier.
Many if not most software packages indicate outliers in a data set by using an
asterisk (*) or star symbol and use the procedure outlined in Step 6 to identify
outliers. However, not all packages use these symbols and procedures; check
to see what your package does before analyzing your data with a boxplot.
A horizontal boxplot for ages of the Best Actress Oscar award winners from
1928–2009 is shown in Figure 7-7. You can see the numbers separating sec-
tions of the boxplot match the five-number summary statistics shown in
Figure 7-3.
Boxplots can be vertical (straight up and down) with the values on the axis
going from bottom (lowest) to top (highest); or they can be horizontal, with
the values on the axis going from left (lowest) to right (highest). The next sec-
tion shows you how to interpret a boxplot.

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