2.13:
Multiple Bar Graph
Suppose one wants to compare the number of male and female students enrolled in different courses—Statistics, Biology, Physics, and Chemistry. Such comparison of data sets with different categories is visualized using a multiple bar graph.
Here, the graph contains two groups of bars separated by a gap to indicate two sets of data.
In this case, the black bars indicate the number of male students, and the teal bars represent the number of female students.
The first set of bars indicates five male and four female students enrolled in Statistics; the second set indicates three male and five female students enrolled in Biology and so on.
In the multiple bar graphs, one can add any number of bars under various categories, depending on the given number of data sets.
Multiple bar graphs help compare and analyze patterns among several categories. From this bar graph, one can easily answer questions such as which day recorded the highest sale of apples or which is the most popular fruit.
2.13:
Multiple Bar Graph
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas frequency is listed along the vertical or y-axis. For example, consider comparing six students based on their marks in four subjects, say, Physics, Chemistry, Biology, and Statistics, then four different bars need to be drawn for each student. Depending on the heights of the bars, the data are analyzed and compared.
Though the multiple bar graph is used for most relational cases, it has some demerits, like it often requires additional explanation and fails to expose key assumptions, causes, impacts, and patterns. Sometimes, it can be easily manipulated and may give false impressions.