The graph will cross the x-axis at zeros with odd multiplicities. The sum of the multiplicities is the degree of the polynomial function. Begin typing your search term above and press enter to search. Press ESC to cancel. Skip to content Home Why is it important to use charts and graphs? Ben Davis June 1, Why is it important to use charts and graphs?
What is the importance of using graph or chart in research? Why do graphs and charts provide a good representation of data? What is the importance of charts? What is the difference between charts and graphs? What makes the chart more attractive and meaningful?
What 5 things should a good graph have? What are the 4 parts every graph must have? What are all the elements of a good graph?
What makes a good bar graph? What are the advantages of using a bar graph? Area graphs are good to use when you are tracking the changes in two or more related groups that make up one whole category for example public and private groups. X-Y plots are used to determine relationships between the two different things. The x-axis is used to measure one event or variable and the y-axis is used to measure the other.
If both variables increase at the same time, they have a positive relationship. Each bar represents one value. When the bars are stacked next to one another, the viewer can compare the different bars, or values, at a glance.
For example, a bar graph might show how smartphone use has changed over time. Along the vertical axis, or axis Y, the maker of the graph would plot a quantitative or numerical scale such as smartphone users by the millions. On the horizontal axis, or axis X, the graph maker might plot a category, such as years from to In this way, viewers can easily see how many millions of people started using smartphones during each of those years and whether that number steadily increased or decreased over time.
Because bar graphs have been in widespread use everywhere from textbooks to newspapers, most audiences understand how to read a bar graph and can grasp the information the graph conveys.
In cases like that, the inherent ordering usually takes precedence. Another consideration is on how you should use color in your bar charts. Certain tools will color each bar differently by default, but this can distract the reader by implying additional meaning where none exists. Instead, color should be used with purpose.
For example, you might use color to highlight specific columns for storytelling. Colors can also be used if they are meaningful for the categories posted e. It may be tempting to replace bars with pictures that depict what is being measured e. If your choice of symbol scales both width and height with value, differences will look much larger than they actually are, since people will end up comparing the areas of the bars rather than just their widths or heights.
However, this growth is exaggerated with the icon-based representation, since the surface area of the icon is more than 2. If you feel the need to use icons to depict value, then a better — though still not great — option is to use the pictogram chart type instead. In a certain sense, this is like changing the texture of its corresponding bar to a repeating image.
One major caution with this chart type is that it can make values harder to read, since the reader needs to perform some mental mathematics to gauge the relative values of each category.
A common bar chart variation is whether or not the bar chart should be oriented vertically with categories on the horizontal axis or horizontally with categories on the vertical axis. In a vertical chart, these labels might overlap, and would need to be rotated or shifted to remain legible; the horizontal orientation avoids this issue. A common addition to bar charts are value annotations. Annotations can report these values where they are important, and are usually placed in the middle of the bar or at their ends.
When the numeric values are a summary measure, a frequent consideration is whether or not to include error bars in the plot. Error bars are additional whiskers added to the end of each bar to indicate variability in the individual data points that contributed to the summary measure.
Since there are many choices for uncertainty measure e. Alternatively, you may wish to depict variance within each category with a different chart type such as the box plot or violin plot. While these plots will have more elements for a reader to parse, they provide a deeper understanding of the distribution of values within each group. One variation of the bar chart is the lollipop chart. It presents exactly the same information as a bar chart, but with different aesthetics.
Instead of bars, we have lines topped by dots at their endpoints. A lollipop chart is most useful when there are a lot of categories and their values are fairly close together. By changing the aesthetic form of the plotted values, it can make the chart much easier to read. While the pie chart is much-maligned, it still fills a niche when there are few categories to plot, and the parts-to-whole division needs to be put front and center. Histograms are a close cousin to bar charts that depict frequency values.
The bars in a histogram are typically placed right next to each other to emphasize this continuous nature: bar charts usually have some space between bars to emphasize the categorical nature of the primary variable.
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