Last updated on June 18th, 2025
Frequency polygons are the graphical visualizations used to understand data distribution in a dataset. They represent values in a distribution through a specific shape. This graphical representation is commonly used in statistics which helps to compare multiple datasets and identify trends and patterns of data. In this topic, we will explore the frequency polygons.
Frequency polygons are a fundamental graphical tool in statistics. They represent the frequency distribution of continuous data. In the late 19th century, the frequency polygon was first presented by an English statistician named Karl Pearson.
A frequency polygon shows the dataset’s distribution across different class intervals. It helps to simplify and organize large datasets for easier understanding. It represents data using a line graph that connects the midpoints of the different intervals.
Now, let us learn how to draw a frequency polygon. Like a regular graph, a frequency polygon has two axes, one is the x-axis, and the next is the y-axis. On both the x-axis and y-axis, the frequency polygon’s curve is depicted. In the x-axis, the various values in a dataset are represented, while the number of occurrences in each category is represented by the y-axis.
While drawing a frequency polygon, the most important aspect to consider is the class intervals. We can depict a frequency polygon with or without a histogram. Histograms are rectangular bars that represent the information or values in a dataset. So, if we draw a frequency polygon with a histogram, we first draw the rectangular bars for each class interval.
Later, connect the bar’s midpoints to form a frequency polygon. Below are some steps, that we should follow while drawing a frequency polygon without a histogram:
Step 1: Mark the class intervals on the x-axis and plot the frequencies on the y-axis.
Step 2: Calculate the midpoints of each class interval, or the class marks.
Step 3: Plot the class marks on the x-axis.
Step 4: Plot the frequency based on each class mark on the height. Be sure to mark it on the same class mark, not at the higher or lower limit of the interval.
Step 5: After marking the points, connect them with line segments. These are similar to a line graph.
Step 6: After this, we get a curve known as the frequency polygon.
While we draw a frequency polygon, we have to calculate the class marks or the midpoints or each class interval. So, the formula for finding the midpoints of the frequency polygon is as follows:
Class mark (Midpoint) = (Upper Limit + Lower Limit) / 2
Data presented in the form of class intervals and frequencies is graphically represented by a frequency polygon graph. It allows us to analyze and compare large datasets while identifying patterns and trends in data distribution. For a better understanding, we can consider an example. Here is a frequency table that shows the goals scored by students in a match:
Goals scored | Frequency |
0 | 3 |
1 | 6 |
2 | 5 |
3 | 7 |
4 | 4 |
5 | 5 |
For the above frequency table, we can plot the frequency polygon by marking the frequency on the x-axis and the goals scored on the y-axis.
The cumulative frequency polygon depicts the cumulative frequencies of a given dataset. In this graph, the dots are plotted at the upper-class borders against the corresponding cumulative frequencies. After that, the dots are connected by a line and result in a cumulative frequency polygon. The accumulation of data over time or intervals is illustrated by these graphs. Now, let us consider an example. Here is a frequency table showing the marks scored by students for the English exam:
Marks scored | Frequency | Cumulative frequency |
0 - 10 | 3 | 3 |
11 - 20 | 5 | 8 |
21 - 30 | 11 | 19 |
31 - 40 | 6 | 25 |
41 - 50 | 3 | 28 |
For the above frequency table, we can plot the cumulative frequency polygon by marking the frequency on the y-axis and the marks scored on the x-axis. When we calculate the cumulative frequency we add up the
frequencies ( 3 + 5 = 8).
Also, add Frequency Polygon Graph and Cumulative Frequency Polygon
A frequency polygon is a graphical representation of data that is represented in the form of class intervals and frequencies. It is similar to histograms, but a frequency polygon uses line segments to join the midpoints of each class interval. The main differences between these two graphs are listed below:
A frequency polygon is a graphical representation of data using line segments, which form a curve. In various fields, the frequency polygons are commonly used to interpret data and identify trends and patterns. Here are some of the real-world applications of frequency polygons:
Drawing a frequency polygon is simple, and the data comparison becomes easier with these types of graphs. However, some common mistakes can happen and will lead to inaccurate data comparison and interpretation. Here are some common errors and their helpful solutions for frequency polygons.
If the pages range of pages students read in a week in a reading club is distributed by 0 - 50, 50 - 100, 100 - 150, 150 - 200, 200 - 250. What would be the class marks for each page range?
0 - 50 = 25
50 - 100 = 75
100 - 150 = 125
150 - 200 = 175
200 - 250 = 225
To calculate the classmark for a frequency polygon graph, we use the formula:
Class mark (Midpoint) = (Upper Limit + Lower Limit) / 2
Hence, Class interval 0 - 50 = 50 + 0 / 2 = 25
Class interval 50 - 100 = 100 + 50 / 2 = 75
Class interval 100 - 150 = 150 + 100 / 2 = 125
Class interval 150 - 200 = 200 + 150 / 2 = 175
Class interval 200 - 250 = 250 + 200 / 2 = 225
If the weight range for a class of 50 students is distributed by 30 - 40, 40 - 50, 50 - 60, 60 - 70. What would be the class marks for each weight range?
30 - 40 → 35
40 - 50 → 45
50 - 60 → 55
60 - 70 → 65
To calculate the classmark for a frequency polygon graph, we use the formula, Classmark = (Upper Limit + Lower Limit) / 2.
Hence, the classmarks 30 - 40 = 40 + 30 / 2 = 35
The classmarks 40 - 50 = 50 + 40 / 2 = 45
The classmarks 50 - 60 = 60 + 50 / 2 = 55
The classmarks 60 - 70 = 70 + 60 / 2 = 65
Jaipreet Kour Wazir is a data wizard with over 5 years of expertise in simplifying complex data concepts. From crunching numbers to crafting insightful visualizations, she turns raw data into compelling stories. Her journey from analytics to education ref
: She compares datasets to puzzle games—the more you play with them, the clearer the picture becomes!