Frequency Table Generator
Count how often each value appears, then read relative and cumulative shares.
Frequency Table Generator
Create detailed frequency distribution tables with statistics for numerical and categorical data
Sample Datasets
What the columns mean
- Frequency: raw count for that value or category
- Relative frequency: count / n (between 0 and 1)
- Percentage: relative × 100
- Cumulative: running total of counts or percents up through that row
Categorical data: one row per label. Continuous data: usually bin into intervals first, then count per bin.
Reading the table
The mode is the row with the highest frequency. Cumulative percent at a cutpoint answers "what share is at or below this value?" Skew shows up as a long tail of rare bins; a flat table means categories are roughly even.
Missing values: decide up front—drop, code as "missing," or impute—then document n. Rare extreme categories may be outliers or real sparse events; do not delete them just because the count is 1.
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Frequently Asked Questions
How do I read cumulative percentages?
At each row, the cumulative % is the share of observations at that value or lower (if sorted). The last row should reach 100% (within rounding).
What about entropy?
Higher entropy means a more even spread across categories; lower means a few categories dominate. Useful as a diversity summary, not a substitute for the table.
Missing or invalid values?
Clean or code them before counting. Report how many you excluded so percentages stay honest.
Outliers in a frequency table?
Isolated high/low bins with tiny counts deserve a second look. Keep genuine rares; fix obvious entry errors.
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