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5.3: Stem and Leaf Plots

  • Page ID
    29463
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    Histograms are one of the most widely used methods for displaying the observed values for a variable. They’re simple, pretty, and very informative. However, they do take a little bit of effort to draw. Sometimes it can be quite useful to make use of simpler, if less visually appealing, options. One such alternative is the stem and leaf plot. To a first approximation, you can think of a stem and leaf plot as a kind of text-based histogram. Stem and leaf plots aren’t used as widely these days as they were 30 years ago since it’s now just as easy to draw a histogram as it is to draw a stem and leaf plot. Not only that, they don’t work very well for larger data sets. As a consequence, you probably won’t have as much of a need to use them yourself, though you may run into them in older publications.

    With all that as background, let's have a look at stem and leaf plots. Here is the stem-and-leaf plot for an exam from several years ago. The minimum score is 0 and maximum score is 89. The Analyze/Descriptives.../Explore function produces the plot:


    Unit4Exam46186 Stem-and-Leaf Plot

     Frequency    Stem &  Leaf

        14.00        0 .  00000000000002
          .00        1 .
         1.00        2 .  2
         1.00        3 .  0
         2.00        4 .  68
         5.00        5 .  34789
        10.00        6 .  1355888999
        15.00        7 .  000224556778999
        22.00        8 .  0112224444566677778999

     Stem width:  10.00
     Each leaf:        1 case(s)

    The values to the left of the . are called stems and the values to the right are called leaves. If you just look at the shape that the leaves make, you can see something that looks a lot like a histogram made out of numbers, just rotated by 90 degrees. But if you know how to read the plot, there’s quite a lot of additional information here. In fact, it’s also giving you the actual values of all of the observations in the data set.


    This page titled 5.3: Stem and Leaf Plots is shared under a CC BY-SA 4.0 license and was authored, remixed, and/or curated by Danielle Navarro.