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Relative Frequency Formula:
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Relative Frequency (RF) is a statistical measure that represents the proportion of times a particular category or value occurs in a dataset relative to the total number of observations. It provides insight into the distribution and composition of categorical data.
The calculator uses the Relative Frequency formula:
Where:
Explanation: The formula calculates the proportion that each category represents within the entire dataset, allowing for comparison across different categories and datasets.
Details: Relative frequency is essential for understanding data distribution patterns, identifying trends, making comparisons between different groups, and converting raw counts into meaningful proportions for statistical analysis and visualization.
Tips: Enter the frequency (count of specific category) and total frequency (sum of all counts). Both values must be positive numbers, and frequency cannot exceed total frequency. The result is displayed as a decimal proportion.
Q1: What is the difference between frequency and relative frequency?
A: Frequency is the actual count of occurrences, while relative frequency is the proportion of that count relative to the total observations, expressed as a decimal or percentage.
Q2: How do I convert relative frequency to percentage?
A: Multiply the relative frequency by 100. For example, RF = 0.25 equals 25%.
Q3: What is the range of relative frequency values?
A: Relative frequency ranges from 0 to 1, where 0 means the category never occurs and 1 means it represents the entire dataset.
Q4: Can relative frequency be greater than 1?
A: No, relative frequency cannot exceed 1 because frequency cannot be greater than total frequency in a valid dataset.
Q5: Why use relative frequency instead of absolute frequency?
A: Relative frequency allows for meaningful comparisons between datasets of different sizes and helps understand the proportional distribution of categories.