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A box and whisker plot is defined as a graphical method of displaying variation in a set of data. In most cases, a histogram analysis provides a sufficient display, but a box and whisker plot can provide additional detail while allowing multiple sets of data to be displayed in the same graph. Why Use a Box and Whisker Plot?
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Box and whisker plots, sometimes known as box plots, are a great chart to use when showing the distribution of data points across a selected measure. These charts display ranges within variables measured. This includes the outliers, the median, the mode, and where the majority of the data points lie in the "box".
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A box and whisker plot is a way of compiling a set of data outlined on an interval scale. It is also used for descriptive data interpretation. The box and whisker plot displays how the data is spread out. In the box and whisker diagram, it has five pieces of information,(also called a five-number summary). Elements of a Box and Whisker Plot
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The box-and-whisker plot was first introduced in 1970 by John Tukey, who later published on the subject in his book "Exploratory Data Analysis" in 1977. [6] Elements Figure 2. Box-plot with whiskers from minimum to maximum Figure 3. Same box-plot with whiskers drawn within the 1.5 IQR value
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Box plots (also called box and whisker charts) provide a great way to visually summarize a dataset, and gain insights into the distribution of the data. In this tutorial, we will discuss what a box plot is, how to make a box plot in Microsoft Excel (new and old versions), and how to interpret the results. Table of Contents
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Let's make a box plot for the same dataset from above. Step 1: Scale and label an axis that fits the five-number summary. Step 2: Draw a box from Q 1 to Q 3 with a vertical line through the median. Recall that Q 1 = 29 , the median is 32 , and Q 3 = 35. Step 3: Draw a whisker from Q 1 to the min and from Q 3 to the max.
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The last point is the maximum value in your data distribution. The box and whiskers plot is summary of our data and often can be used to identify low and high outliers. For instance, to find a low outlier, we can use the equation: Q1 - 1.5 (Q3-Q1). To find a high outlier, we can use the equation: Q3 + 1.5 (Q3-Q1).
