Distribution
Complementary cumulative distribution plot (CCDF)
Shows the proportion of observations exceeding each value.
- Plot
- Distribution
- Tail probability
What it answers
What proportion exceeds a threshold?
- Shape
- Non-increasing step curve
- What it shows
- Tail mass and rare large values.
Example
The question
How many customers stay longer than a given number of months?
Share of customers still active after each month (CCDF, illustrative)
Illustrative data. It belongs to no company.
When to use it
Use it when
Tail probabilities and exceedance analysis.
Avoid it when
When strict versus inclusive threshold rules are not stated.
How to read it
Read a threshold and the remaining share above it.
Note
State strict or inclusive exceedance.
The data you need
- Layout
- Long table
- Fields
- Value: number
- One row is
- One raw numeric observation. What is the grain?
- Rules
- Use raw observations unless the chosen renderer explicitly accepts summaries or bins. Define missing-value treatment.
- Optional
- Optional frequency Weight. Pre-binned alternative: BinStart,BinEnd,Count; do not treat counts as raw values.
- Preparation
- Keep raw numeric observations, group them if needed and handle missingness explicitly. Choose bins, bandwidth or summary conventions as appropriate to this specific plot.
- Units
- Y is count(Value>threshold)/eligible nonmissing observations under strict exceedance; state alternatives.
Worked example
Values 10,12,12,18,23. For strict exceedance above 12,2/5=40% remain.
| Value |
|---|
| 10 |
| 12 |
| 12 |
| 18 |
| 23 |