Relationship

Scatterplot matrix / pair plot

Shows pairwise relationships among numeric variables, often with distributions on the diagonal.

Plot
Relationship
Multivariate exploration

What it answers

Which variable pairs show relationships?

Shape: Grid of pairwise scatterplots
What it shows: Pairwise associations and multivariate outliers.

Example

The question

Which pairs of store measures move together?

Floor space, staff and sales, every pair (scatterplot matrix, illustrative)

A 3 by 3 grid: each panel plots one measure against another; the diagonal shows each measure’s histogram. All three measures rise together, sales and floor space most closely.

Illustrative data. It belongs to no company.

When to use it

Use it when

Exploring several numeric variables jointly.

Avoid it when

Too many variables or too few observations.

How to read it

Read row and column variable names, then compare each scatter cell.

The data you need

Layout: Wide observation table
Fields: ObservationID: text; V1...Vp: numeric columns
One row is: One observation with several measured numeric variables. What is the grain?
Rules: Align variables by observation. Decide scaling, missing-data handling and which columns are features.
Optional: Optional Group. PCA, clustering and correlations require calculations before rendering their derived results.
Preparation: Align measurements by observation ID, select relevant variables and handle missingness. Scale or standardise only where the analytical method requires it.
Units: State the unit of every coordinate/variable. A unitless transformed or standardised scale must be identified; no common denominator is assumed.

A wide table: one row per item, with a separate column for each measure or period.

Worked example

Three variables X, Y, Z for three records produce three unique pairwise relationships. A repeated X-Y pattern can be inspected alongside X-Z.

ID	V1	V2	V3
A	10	30	5
B	12	20	8
C	18	25	6

All charts in the library