Distribution
Kernel density plot
Estimates continuous probability density from numeric observations using a kernel.
- Plot
- Distribution
- Comparison
What it answers
Where is probability density concentrated?
- Shape
- Smooth estimated-density curve
- What it shows
- Modes, tails and relative concentration.
Example
The question
Where do the team’s monthly salaries mostly fall?
Monthly salaries in the team, 75 people (density estimate, illustrative)
Illustrative data. It belongs to no company.
When to use it
Use it when
Smooth exploration of continuous numeric values.
Avoid it when
Tiny samples, bounded values without suitable treatment or hidden bandwidth choices.
How to read it
Read high curve regions as greater density, not probability at a single point.
Note
Bandwidth changes the visible shape.
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
- The measurement axis retains its original unit. Frequency uses observations; normalised frequency divides by group sample size; density also depends on interval width.
Worked example
Example values 10,12,12,18,23. Fit a stated kernel and bandwidth. A bump near 12 may appear; its size is smoothing-dependent.
| Value |
|---|
| 10 |
| 12 |
| 12 |
| 18 |
| 23 |