Frequency Table Calculator

A frequency table is a summary of data that shows how often each unique value appears in a dataset. It organizes raw data into a structured format displaying each value alongside its frequency count, relative frequency, and cumulative frequency, making it easy to identify patterns and distributions.

Relative frequency is the proportion of times a value occurs compared to the total number of observations. It is calculated by dividing a value's frequency by the total count (n). For example, if a value appears 4 times in a dataset of 20, its relative frequency is 4/20 = 0.20, or 20%.

Cumulative frequency is the running total of frequencies up to and including a particular value. It is calculated by adding each frequency to the sum of all preceding frequencies. The last entry in a cumulative frequency column always equals the total number of data points (n).

Cumulative relative frequency is the accumulation of previous relative frequencies up to and including a given value. It tells you what proportion of the data falls at or below a certain value. The final cumulative relative frequency is always equal to 1 (or 100%).

You can enter data values separated by commas, spaces, or new lines. For example: '5, 3, 8, 5, 3' or '5 3 8 5 3'. The calculator accepts any numeric values including decimals and negative numbers. Simply paste data directly from a spreadsheet if needed.

The mode is the value that appears most frequently in your dataset. In a frequency table, the mode corresponds to the value with the highest frequency count. A dataset can have one mode (unimodal), two modes (bimodal), or more (multimodal) if multiple values share the highest frequency.

A standard frequency table lists individual data values and their counts, best suited for discrete or categorical data with a limited number of unique values. A grouped frequency table aggregates data into class intervals (e.g., 0–10, 10–20), which is more practical for large continuous datasets with many distinct values.

This calculator is designed for numeric data values. For categorical data such as letter grades or labels, you would need to encode them as numbers or use a dedicated categorical frequency tool. The calculator handles both integers and decimal numbers.