Charts, distributions and measurement
Scatter and perceptual plots
Points in two dimensions: correlation, clusters and outliers from declared records.
chart.basic@1New DDN source
Why use it?
Averages hide structure that only the cloud shows.
When it helps
Exploring relationships between two measures.
How to read it
- Each point is one record on two axes.
- A cluster is a hypothesis to investigate, not a conclusion.
What you need
- The two measures with units
- The records
- Any grouping
Things to check
- Do not read correlation as causation.
- Do not crop an outlier out of the frame silently.
Scope of this diagram
Declared points; no regression or clustering is computed by this view.
Work with the converted example
- Open the viewer and select
scatter. Inspect the rendered view and its source. - Open the designer. Keep the selected profile and inspect its legal palette and property controls.
- Change one displayed title or an appropriate data value. Apply the edit and check the resulting diagram.
- Run the verifier and review its diagnostics before moving complete content into review.
- Download your edited DDN source to retain the change.
The example uses the indentation-based ddn-next-1 dialect. The language guide explains reusable definitions, dotted data rows and explicit property blocks.