Quick start
- Paste grouped classes or raw scores, then adjust width/start.
- Unequal widths? The histogram switches to frequency density so bar area still equals frequency.
- Copy a shareable URL or export CSV/SVG for handouts.
All parsing and plotting stay in your browser. No uploads.
Inputs & settings
| Lower bound L | Upper bound U | Frequency f |
|---|
Results
Graphs
Histogram
Cumulative frequency (ogive)
Summary
Bar area tracks frequency, and ogive points sit on each upper boundary.
Steps & reasoning
Share & export
FAQ
Should histogram height use frequency or frequency density?
If all widths match, frequency works because bar area tracks count. When widths differ, use frequency density so each bar area equals its frequency.
Where do ogive points go?
At each upper boundary with the cumulative frequency there, matching classes defined as [L, U).
What do relative and cumulative relative frequency mean?
Relative frequency is f/N; cumulative relative is the running total. The final value is always 1 (100%).
Why highlight modal or median classes?
They show where the distribution peaks and where half the observations accumulate. With unequal widths, the modal class uses the highest frequency density.
Is any data uploaded?
No. Everything runs locally, and the share URL only stores parameters in the query string.
How to use Histogram & cumulative frequency from grouped data effectively
What this calculator does
This page is for estimating outcomes by changing inputs in one controlled workflow. The model keeps your focus on variables, not output shape. Start with stable assumptions, then test sensitivity by changing one key input at a time to observe directional impact.
Input meaning and unit policy
Each input has an expected unit and a typical range. For reliable interpretation, check whether you are using the same unit system, period, and base assumptions across all runs. Unit mismatch is the most common source of unexpected drift in numeric results.
Use-case sequence
A practical sequence is: first run with defaults, then create a baseline log, then run one alternative scenario, and finally compare only the changed output metric. This sequence reduces cognitive load and prevents false pattern recognition in early experiments.
Common mistakes to avoid
Avoid changing too many variables at once, mixing incompatible data sources, and interpreting a one-time output without checking robustness. A single contradictory input can flip conclusions, so keep each experiment minimal and document assumptions as part of your note.
Interpretation guidance
Review both magnitude and direction. Direction tells you whether a strategy moves outcomes in the desired direction, while magnitude helps you judge practicality. If both agree, you can proceed; if not, rebuild the baseline and verify constraints before deciding.