Enter your categories or values and their corresponding absolute frequencies below. The calculator will then generate a relative frequency table, showing the proportion of each category within the total dataset.
var rowCount = 4; // Initialize with the number of pre-filled rows
function addRow() {
rowCount++;
var container = document.getElementById('inputRowsContainer');
var newRow = document.createElement('div');
newRow.className = 'input-row';
newRow.id = 'row_' + rowCount;
newRow.innerHTML = `
`;
container.appendChild(newRow);
}
function calculateRelativeFrequencies() {
var data = [];
var totalAbsoluteFrequency = 0;
var resultDiv = document.getElementById('result');
resultDiv.innerHTML = "; // Clear previous results
for (var i = 1; i <= rowCount; i++) {
var categoryNameInput = document.getElementById('categoryName_' + i);
var absoluteFrequencyInput = document.getElementById('absoluteFrequency_' + i);
if (categoryNameInput && absoluteFrequencyInput) {
var category = categoryNameInput.value.trim();
var frequency = parseFloat(absoluteFrequencyInput.value);
if (category === '') {
category = 'Category ' + i; // Default name if left blank
}
if (isNaN(frequency) || frequency < 0) {
resultDiv.innerHTML = 'Please enter a valid non-negative number for Absolute Frequency in row ' + i + '.';
return;
}
data.push({ category: category, frequency: frequency });
totalAbsoluteFrequency += frequency;
}
}
if (data.length === 0) {
resultDiv.innerHTML = 'Please add at least one category and frequency.';
return;
}
if (totalAbsoluteFrequency === 0) {
resultDiv.innerHTML = 'The total absolute frequency cannot be zero. Please enter positive frequencies.';
return;
}
var resultsHtml = '
Relative Frequency Table
';
resultsHtml += '
';
resultsHtml += '
Category/Value
Absolute Frequency
Relative Frequency (Decimal)
Relative Frequency (%)
';
resultsHtml += '';
var totalRelativeFrequencyDecimal = 0;
var totalRelativeFrequencyPercentage = 0;
for (var j = 0; j < data.length; j++) {
var relativeFrequencyDecimal = data[j].frequency / totalAbsoluteFrequency;
var relativeFrequencyPercentage = relativeFrequencyDecimal * 100;
totalRelativeFrequencyDecimal += relativeFrequencyDecimal;
totalRelativeFrequencyPercentage += relativeFrequencyPercentage;
resultsHtml += '
A relative frequency table is a powerful statistical tool used to display the proportion or percentage of times each value or category appears in a dataset. Instead of just showing the raw counts (absolute frequencies), it provides context by showing how each count relates to the total number of observations. This makes it easier to compare different categories and understand the distribution of data.
What is Relative Frequency?
Relative frequency is simply the fraction or proportion of times a specific event or value occurs in a dataset. It is calculated by dividing the absolute frequency (the count of how many times a value appears) by the total number of observations in the dataset.
The formula for relative frequency is:
Relative Frequency = (Absolute Frequency of a Category) / (Total Number of Observations)
This value is typically expressed as a decimal between 0 and 1, or as a percentage (by multiplying the decimal by 100).
Why Use a Relative Frequency Table?
Relative frequency tables are incredibly useful for several reasons:
Comparison: They allow for easy comparison of the prevalence of different categories, even if the total sample sizes differ across datasets.
Proportionate Understanding: They give a clear picture of the proportion each category contributes to the whole, which is often more insightful than just raw counts.
Probability Estimation: In many cases, relative frequencies can be used as estimates for the probability of an event occurring.
Data Visualization: They form the basis for creating various charts like pie charts and bar charts, which visually represent proportions.
How to Construct a Relative Frequency Table (Manual Example)
Let's say a survey was conducted asking 20 people about their favorite primary color. The results were:
Red: 5 people
Blue: 8 people
Green: 4 people
Yellow: 3 people
The total number of observations is 5 + 8 + 4 + 3 = 20.
Now, let's calculate the relative frequency for each color:
Red: Absolute Frequency = 5. Relative Frequency = 5 / 20 = 0.25 (or 25%)
Blue: Absolute Frequency = 8. Relative Frequency = 8 / 20 = 0.40 (or 40%)
Green: Absolute Frequency = 4. Relative Frequency = 4 / 20 = 0.20 (or 20%)
Yellow: Absolute Frequency = 3. Relative Frequency = 3 / 20 = 0.15 (or 15%)
The sum of all relative frequencies should always be 1 (or 100% when expressed as percentages). In our example: 0.25 + 0.40 + 0.20 + 0.15 = 1.00.
Using the Relative Frequency Table Calculator
Our online calculator simplifies this process. To use it:
Enter Categories/Values: In the "Category/Value" field, type the name of each distinct category or value from your dataset (e.g., "Red", "Blue", "Male", "Female", "A", "B", "C").
Enter Absolute Frequencies: For each category, enter its corresponding "Absolute Frequency," which is the count of how many times that category appears in your data.
Add More Rows: If you have more categories than the initial input fields, click the "Add Another Row" button to add more input pairs.
Calculate: Once all your categories and their absolute frequencies are entered, click the "Calculate Relative Frequencies" button.
The calculator will instantly generate a table showing each category, its absolute frequency, its relative frequency as a decimal, and its relative frequency as a percentage. It will also provide totals for verification.
This tool is ideal for students, researchers, and anyone needing to quickly analyze and present the distribution of categorical or discrete data.