In a pie graph, each slice of the pie represents a share of the total, or a percentage. The three pie graphs in Figure 4 show that the share of the U.
The pie graphs allow you to get a feel for the relative size of the different age groups from to to , without requiring you to slog through the specific numbers and percentages in the table. Some common examples of how pie graphs are used include dividing the population into groups by age, income level, ethnicity, religion, occupation; dividing different firms into categories by size, industry, number of employees; and dividing up government spending or taxes into its main categories.
A bar graph uses the height of different bars to compare quantities. The table, below, lists the 12 most populous countries in the world. Figure 5 provides this same data in a bar graph.
The height of the bars corresponds to the population of each country. Although you may know that China and India are the most populous countries in the world, seeing how the bars on the graph tower over the other countries helps illustrate the magnitude of the difference between the sizes of national populations. Figure 5. The graph shows the 12 countries of the world with the largest populations.
The height of the bars in the bar graph shows the size of the population for each country. Bar graphs can be subdivided in a way that reveals information similar to that we can get from pie charts.
Figure 6 offers three bar graphs based on the information from Figure 4 about the U. Figure 6 a shows three bars for each year, representing the total number of persons in each age bracket for each year. Figure 6 b shows just one bar for each year, but the different age groups are now shaded inside the bar.
In Figure 6 c , still based on the same data, the vertical axis measures percentages rather than the number of persons. In this case, all three bar graphs are the same height, representing percent of the population, with each bar divided according to the percentage of population in each age group.
It is sometimes easier for a reader to run his or her eyes across several bar graphs, comparing the shaded areas, rather than trying to compare several pie graphs. Figure 5 and Figure 6 show how the bars can represent countries or years, and how the vertical axis can represent a numerical or a percentage value. Bar graphs can also compare size, quantity, rates, distances, and other quantitative categories. Now that you are familiar with pie graphs, bar graphs, and line graphs, how do you know which graph to use for your data?
Pie graphs are often better than line graphs at showing how an overall group is divided. However, if a pie graph has too many slices, it can become difficult to interpret. Bar graphs are especially useful when comparing quantities. For example, if you are studying the populations of different countries, as in Figure 5, bar graphs can show the relationships between the population sizes of multiple countries. Not only can it show these relationships, but it can also show breakdowns of different groups within the population.
A line graph is often the most effective format for illustrating a relationship between two variables that are both changing. Alternatively, you can connect through missing values or use a dotted line for the connection across missing values.
It is important to be aware of missing values and how you display them in your graphic. The graph in Figure 3 shows a scatter plot of two continuous variables.
The x-axis shows body weight; the y-axis shows sleep time. The graph also shows the points connected with a line, which is not correct. The points are for different species of animals and do not have a relationship that shows changes over time. The graph in Figure 4 shows a scatter plot with a simple linear regression, which is a correct way to display these data.
When creating a line graph, or any graph, be cognizant of your scales. For instance, in the past, books recommended including zero on the y-axis. Current practice dictates using zero only when it makes sense for your data. Figure 5 shows historical data for hotel room occupancy rates in Australia for the fourth quarter of several years.
The y-axis follows the historical recommendation of an axis that starts at zero. The problem with this approach is that it minimizes the visual impact of year-to-year differences.
Compare Figures 5 and 6. Figure 6 uses a more reasonable set of values for the y-axis range so that it is easier to see the peak in Most software tools automatically create a y-axis that makes sense for the data.
Some software tools allow you to change the axes. Line graphs can include multiple lines. The graph in Figure 7 shows historical market share data for smart phone operating systems from when the first smartphones were released until Each line shows the change over time for the different operating systems.
When creating line graphs with multiple lines, be sure to consider the colors used, based on how the graph will be shared and viewed. Will it always be in color? Will it be in black and white?
The message is clearly stated in the title, and each of the line graphs is properly labelled. It is easy to see from this chart that the total cell phone use has been rising steadily since , except for a one-year period where the numbers drop slightly.
The pattern of use for women and men seems to be quite similar with very small discrepancies between them. Please contact us and let us know how we can help you. Table of contents. Topic navigation. In summary, line charts: show specific values of data well, reveal trends and relationships between data, compare trends in different groups. Example 1 — Plotting a trend over time Chart 5. Data table for Chart 5. The information is grouped by Month appearing as row headers , Number of students appearing as column headers.
The information is grouped by Month appearing as row headers , Number of crime offenders appearing as column headers. The information is grouped by Year appearing as row headers , Number of men thousands , Number of women thousands and Total number thousands appearing as column headers. Year Number of men thousands Number of women thousands Total number thousands Statistics: Power from Data! Report a problem on this page.
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