Relative Frequency Calculator
Calculate experimental probability, percentages, and frequency distributions from data
Calculate Relative Frequency
Individual data points (frequency will be calculated automatically)
Separate values with commas. Can be numbers or text.
Example: Survey Response Analysis
Customer Satisfaction Survey
Responses: Excellent, Good, Good, Fair, Excellent, Good, Poor, Excellent, Good, Fair
Total Responses: 10
Frequency Count:
- • Excellent: 3 times
- • Good: 4 times
- • Fair: 2 times
- • Poor: 1 time
Relative Frequencies
Excellent: 3/10 = 0.30 = 30%
Good: 4/10 = 0.40 = 40%
Fair: 2/10 = 0.20 = 20%
Poor: 1/10 = 0.10 = 10%
Interpretation: 70% of customers rated the service as Good or Excellent
Key Concepts
Relative Frequency
Fraction of times an event occurs
Experimental Probability
Likelihood based on actual data
Cumulative Frequency
Running total of frequencies
Common Applications
Survey data analysis and market research
Medical research and clinical trials
Probability experiments and gaming
Quality control and manufacturing
Business analytics and performance tracking
Educational assessment and grading
Understanding Relative Frequency
What is Relative Frequency?
Relative frequency is a statistical measure that shows how often a particular event or value occurs relative to the total number of observations. It's calculated by dividing the frequency of an individual event by the total frequency of all events.
Why is it Important?
- •Provides proportional understanding of data
- •Enables comparison between different datasets
- •Forms the basis for experimental probability
- •Helps identify patterns and trends
Calculation Methods
Basic Formula
Relative Frequency = Individual Frequency ÷ Total Frequency
Always results in a value between 0 and 1
Percentage Conversion
Percentage = Relative Frequency × 100
Converts decimal to percentage form
Cumulative Frequency
CF = Sum of frequencies up to current value
Running total of all frequencies
Types of Frequency Analysis
Ungrouped Data
- • Individual data points
- • Each value counted separately
- • Suitable for discrete or categorical data
- • Example: Survey responses, test scores
Grouped Data
- • Data organized in intervals/classes
- • Frequencies provided for each group
- • Suitable for continuous data
- • Example: Age groups, income brackets
Experimental vs. Theoretical Probability
Experimental Probability
Based on actual observations and data collection
Example: Flipping a coin 100 times and getting 47 heads = 47% experimental probability
Theoretical Probability
Based on mathematical expectations and perfect conditions
Example: Theoretical probability of heads = 50% (assuming fair coin)