![]() ![]() Is the data normally distributed? Is it shaped like a bell curve? You should be able to identify the range, the median, the quartiles, as well as any potential outliers.įinally, the stem plot should also give you an idea of the shape of the distribution of the data. So if you have a value of 25, 2 is the stem that goes on the left of the vertical line and 5 is the leaf that goes on the right.įrom the stem plot it should be easy to describe the distribution of the data. ![]() The stems are usually the first digit of a number. The stems are on the left of the vertical line and the leaves are on the right. In a stem plot you have a vertical line dividing the stems from the leaves. Now that we know what stem plots are and how they are useful, how do we actually construct a stem plot? What do we do with a stem plot, or how do we interpret it? Steps to Interpreting a Stem Plotįirst you should know how to construct a stem plot. It would be quite cumbersome to plot out by hand hundreds of values. However, as you can probably guess, a main disadvantage of the stem plot is that it is really only useful with relatively small data sets. The primary advantage of a stem plot is that rather than condensing our data into points or into bars on a graph, we can see the original numerical values of the data. Other ways to summarize univariate data include a histogram and pie chart. The stem plot is one method of summarizing univariate data visually. Most importantly, the stem plot is useful because it can help with finding the median, mode, and quartiles of data, the range, minimum and maximum values, as well as the most and least frequently occurring observed values in the data. Because in AP® Statistics we are interested in normally distributed data, or a bell curve distribution, the stem plot is an easy and fast way to get a general feel of the distribution especially if the data has relatively few observations. The stem-and-leaf plot or stem plot, for short, is a way to quickly create a graphical display of quantitative data to get an idea of its shape. There are many different ways to get to know data, and you are probably most familiar with calculating central tendencies and measures of dispersion.Īnother thing we are interested in when describing data is its shape, which can be important for determining whether a variable is appropriate for a particular analysis later on. This is done so that you can get to know your data, find errors in data collection and data entry, and to find out basic information such as the central tendencies and dispersion characteristics of data. There is an outlier at 123.In statistics, descriptive data analysis must always be done first before anything else. Th e shape is not very clear because there are only 4 bars. The shape is not very clear because there is only 3 bars. Graph stemplots for the two different samples of grades. A stemplot is given below: legend 6|3 means 63Ī) How many data are there? _ 2 + 2 + 5 + 2 = 11ī) What is the lowest and highest data?_ lowest value is 63, highest is 99Ĭ) What is the list of data? _The whole list of data can be recovered as 63, 65, 78, 79, 81, 82, 82, 85, 89, 91, 99.Įx2. If data consists of decimal, use this online stemplot maker: Įx1. Note: do not skip a stem with no leave to show distribution and outliers correctly. It can be used to show distribution of data (look side way). Back-to-back stem plot can be used to compare two datasets. Data are arranged in order with the same stem in a row. Also, stemplot can easily be created without use of technology.Įach data value is separated into stem and leaf (the last digit). But data values can be recovered from a stemplot. The main problem is data value must be in a relatively small range. It can show the overall pattern and outliers. Stemplot is a quick way to graph relatively small quantitative data. ![]()
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