Merits And Demerits Of Frequency Polygon
Frequency polygon is a graphical representation of data that is used to show the frequency distribution of a dataset. It is a type of histogram, where the data is represented by a line instead of bars. Frequency polygons are widely used in statistics, research, and data analysis. In this article, we will discuss the merits and demerits of frequency polygon.
Merits of Frequency Polygon
1. Easy to Understand
Frequency polygons are easy to understand and interpret. They provide a clear visual representation of the data, which makes it easy to analyze and draw conclusions. The line in a frequency polygon connects the midpoints of the tops of the bars in a histogram. This makes it easy to compare the data and identify patterns.
2. Better than Histograms
Frequency polygons are considered better than histograms because they are smoother and continuous. Histograms are made up of bars, which can sometimes give a false impression of gaps or jumps in the data. Frequency polygons, on the other hand, provide a continuous representation of the data, which makes it easier to identify trends and patterns.
3. Shows Relative Frequencies
Frequency polygons show relative frequencies, which is a better way to compare data. Relative frequency is the proportion of the data that falls into a particular category. This can be expressed as a percentage or a decimal. By showing relative frequencies, frequency polygons provide a better understanding of the data.
4. Useful in Predictive Analysis
Frequency polygons are useful in predictive analysis. They can be used to predict future trends and patterns based on past data. By analyzing the frequency polygon, one can identify the direction of change and make informed decisions.
Demerits of Frequency Polygon
1. Can be Misleading
Frequency polygons can be misleading if not used correctly. They can be manipulated to show a false impression of the data. For example, by adjusting the scale, one can make small changes look significant. It is important to use frequency polygons carefully and correctly.
2. Can be Time-Consuming
Frequency polygons can be time-consuming to create. They require a lot of data and analysis, which can be time-consuming. If the data is not properly analyzed, the frequency polygon can be inaccurate.
3. Requires Expertise
Frequency polygons require expertise in statistics and data analysis. If the person creating the frequency polygon does not have the necessary expertise, it can lead to inaccurate results. It is important to have the right skills and knowledge to create an accurate frequency polygon.
4. Limited to One Variable
Frequency polygons are limited to one variable. They cannot be used to analyze multiple variables at once. If there are multiple variables, multiple frequency polygons need to be created, which can be time-consuming and complex.
Conclusion
In conclusion, frequency polygons are an effective way to represent data. They provide a clear visual representation of the data, which makes it easy to analyze and draw conclusions. However, frequency polygons can be misleading if not used correctly. They require expertise in statistics and data analysis. Despite the demerits, frequency polygons are widely used in research, statistics, and data analysis. They are a valuable tool for analyzing and understanding data.
Remember to use frequency polygons carefully and correctly to get accurate results.
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