We come across the term “frequency” in statistics. Collecting the data, presenting the data in a particular format and analyzing the data is called Statistics. This data collected can be analyzed and represented through various means like graphs, tables, pie charts, histograms etc. In simple terms, frequency is the number of times a number or value repeats in the given data. A frequency distribution table in statistics tell us comprehensively what the different variables present in the entire data and how many times they occur i.e, frequency of the variables.
What is Cumulative Frequency Distribution?
The distribution of any data using a table or graph makes the data comprehensive. There are different classes and subclasses, which indicates the frequency, and this is known as the frequency distribution of data. The frequency is actually the number of times an observation occurs for a particular time period.
Cumulative frequency helps to determine the number of operations, lying above specific observation. For calculating the increasing frequency of any data, the frequency of the observation is added to the sum of frequencies of the predecessors. This previous sum is called the cumulative frequency distribution of the entire set of data.
How to Find Cumulative Frequency and Why to Use Graphs to Represent it
While graphical representation makes the data simpler to understand, one can even see the fluctuations and ups and downs easily by drawing graphs. Usually, bar graphs and frequency polygons are used for graphical representation of the frequencies. The graphical representation of cumulative incidence is called ogive.
The graph for the frequencies can be plotted in two different ways.

Cumulative frequency distribution curve, less than type

Cumulative frequency distribution curve, more than type
The belowgiven solution will provide a stepwise understanding of how to find more than the cumulative distribution frequency and how to calculate less than type cumulative distribution frequency.
How to Calculate Cumulative Frequency Distribution
How to Calculate More Than Cumulative Frequency
The Daily Income of Workers (in rupee) 
No.of Workers (F) 
Lower limits 
Cumulative Frequency (cf) 
100120 
10 
100 
50 
120140 
12 
120 
5010= 40 
140160 
15 
140 
4012= 28 
160180 
13 
160 
2815= 13 
In the abovegiven table, four different groups are representing the daily wage of workers, along with the number of workers in the company. For example, when looking at the first group in the table, there are ten workers with regular income between 100120 rupees.
Step 1: The number of worker columns is the frequency column. Frequency is nothing but the rate at which activity has occurred, which is further denoted by F. The sum of all the frequencies is 50. Cumulative frequency is the running frequency of every group present in the table.
Step 2: As we are solving more than one type, it’s essential to take the lower limits from the table, which is 100,120,140 and 160. If we calculate the lower bound from the table, it will give a result like more than 100, more than 120, and so on.
Step 3: As the total frequency, we have obtained 50; the very first cumulative frequency will be written as 50. The cumulative frequency of the second row will be more than 120 and will be under 180. Hence, we have to subtract ten from 50 and progressively subtract the frequency from the resultant cumulative frequency. In more than cumulative type frequency, the last frequency should match with the cumulative frequency.
Plotting the Frequency Distribution Graph
To draw the cumulative frequency distribution graph, the following steps have to be taken care of:

As the cumulative frequency is a dependent variable, it will come on the Yaxis, and the daily income will be shown on the Xaxis. If there’s a discontinuity of the numbers on the xaxis, then a key should be made before all the numbers. Don’t forget to set the scale before jotting the graph. Start plotting the points with the help of numbers given on the table.

After joining all the points through the freehand, the frequency distribution curve will be a decreasing curve, because in more than one type the curve which will be obtained will always be a decreasing curve.
Graph for Cumulative Frequency Distribution More than Type
How to Calculate Less than Cumulative Frequency?
The Daily Income of Workers (in rupee) 
No.of Workers (F) 
Upper Limits 
Cumulative Frequency (cf) 
100120 
10 
120 
10 
120140 
12 
140 
10+12= 22 
140160 
15 
160 
22+15= 37 
160180 
13 
180 
37+13= 50< /span> 
The abovegiven example is similar to the more than type, but there are some changes made. The class size of every group is 20. Over here, we will be using the upper limits and not the lower limits.
Step 1 and Step 2 are the same as the more than type frequency. Hence we will straight away move to step 3.
Step 3: The frequency and the cumulative frequency for the first group will be the same, which is 12. For the second group, one has to add the frequencies between 100140, that’s 10+12, 22. In the end, the cumulative incidence obtained will be 50. The total frequency will always be equal to the last cumulative frequency.
Plotting the Frequency Distribution Graph
To draw a cumulative frequency distribution graph, The Xaxis will be of daily income, in rupees and the Yaxis will be of cumulative frequency. After setting the scale, start to plot the points on the graph. After making a freehand curve, the frequency distribution curve will be an increasing curve, unlike the more than type graphs.
Calculating the median is also of less than cumulative type frequency is as same as more than type frequency graph.
Note: A sigma (Σ) denotes frequency.
Importance of Learning Cumulative Frequency in Statistics
Cumulative frequency is one of the important features of statistics. It is used to represent the raw data better and understand the data comprehensively and easily. It helps in a proper detailed study in a field study, as it is a positivistic tool, the chance of error is less.
Quantitative data which we exactly need is collected by statistics and it improves our research. Many daily life events like weather forecasts, census, thermal graphs are dependent on statistics and related concepts. Even the samples collected to analyze data related to pandemics are based on statistical techniques like cumulative frequency.