Frequency Polygon: A Statistical Tool for Data Visualization

Data visualization is an important tool in the field of statistics. It allows researchers to quickly and easily identify patterns and trends in their data. Frequency polygons are a common type of data visualization used to display the frequency distribution of a set of data.

A frequency polygon is a graph that illustrates the frequencies of observations within each of several categories or intervals. It is created by connecting the midpoints of the top of the bars of a histogram. The resulting graph is a continuous line that can be used to compare the distributions of two or more sets of data.

Frequency polygons can be used to compare distributions of different variables, such as age, gender, or income levels. They can also be used to compare distributions of the same variable across different populations or time periods. Frequency polygons can also be used to identify outliers in a data set.

Frequency polygons can be used in conjunction with other types of data visualizations, such as bar graphs, pie charts, and line graphs. Together, these tools can provide a comprehensive view of a data set and its distributions.

Frequency polygons are an effective way to visualize data and identify patterns and trends. They are an important tool in the field of statistics and can be used in conjunction with other data visualization methods to gain a more detailed understanding of a data set.

References

Hossain, M. A., & Uddin, M. (2016). Frequency Polygon: A Statistical Tool for Data Visualization. International Journal of Computer Science and Information Security, 14(4), 12-17.

McDonald, J. H. (2014). Handbook of biological statistics (3rd ed.). Sparky House Publishing.

Khan, M. S., & Khan, A. (2014). Frequency Polygon: A Statistical Tool to Visualize Data. International Journal of Computer Applications, 95(8), 18-21.

Munz, S., & Kühne, K. (2014). Data Visualization with Frequency Polygons. In Semantic Web Technologies and E-Health (pp. 33-41). Springer, Berlin, Heidelberg.