Cumulative Frequency Calculator

Cumulative frequency is a running total of frequencies. For each class or interval, you add its frequency to the sum of all previous frequencies. The final cumulative frequency always equals the total number of observations (N).

Cumulative frequency helps you understand how observations accumulate across classes. It's used to determine medians, percentiles, and quartiles, and to draw ogive (cumulative frequency) curves that show the distribution of data at a glance.

Start with the frequency of the first class — that is also its cumulative frequency. For the second class, add its frequency to the first cumulative frequency. Continue this process for each subsequent class. The last cumulative frequency value equals the total frequency N.

Relative cumulative frequency expresses each cumulative frequency as a percentage of the total frequency N. It tells you what proportion of observations fall at or below a given class. For example, a relative cf of 75% means three-quarters of observations fall within or before that class.

Use a cumulative frequency calculator when you need to quickly build a frequency table, find medians or percentiles in grouped data, or prepare data for an ogive chart. It's especially useful in statistics courses, data analysis, and quality control work.

An ogive (also called a cumulative frequency curve) is a line or area graph where the x-axis represents class boundaries and the y-axis shows cumulative frequencies or relative cumulative frequencies. It visually shows how data accumulates and helps locate medians and quartiles.

Type your frequency values separated by commas in the Frequency Values field — for example: 4, 7, 12, 9, 6. Optionally, enter matching class labels (like '0–10, 10–20, 20–30') in the Class Labels field. If no labels are entered, classes are numbered automatically (Class 1, Class 2, etc.).

This calculator works best with frequency counts for grouped data (i.e., you already know how many observations fall in each class). If you have raw data, first tally how many values fall into each category or interval, then enter those counts as your frequency values.