With categorical data, the sample is often divided into groups, and the responses might have a defined order. Categorical or nominal data: use bar chartsīox plots do not make sense for categorical or nominal data, since they are measured on a scale with specific values. Some examples of continuous data are:įor all of these examples, a box plot is an appropriate graphical tool to explore the distribution of the data. Figure 9 shows separate side-by-side box plots for men and women.Ĭontinuous data: appropriate for box plotsīox plots make sense for continuous data, since they are measured on a scale with many possible values. (For more on this data, see the two-sample t-test page.) The variable Body Fat is continuous, so a box plot is an appropriate method for displaying the distribution of the data. Most guidelines expect a difference between body fat for men and for women. One way to measure a person’s fitness is to measure their body fat percentage. If your data have groups, you might learn more about the data by creating side-by-side box plots, providing a simple, yet powerful, tool to compare groups. For example, if the three outliers in Figure 8 are outside the expected range of values, you would need to determine if they are valid data points or not. You may find that the outliers are errors in your data or you may find that they are unusual for some other reason. The outliers affect the mean, median, and other percentiles. Because extreme points are highlighted in a box plot, you can easily identify the data points for investigation. The variable Calories is continuous, so a box plot makes sense. The cereal data in the box plot below shows results from measuring calories per serving for 76 types of cereal. ![]()
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