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Measure · Tool guide 24 of 40

Scatter Plot

A scatter plot displays paired numerical observations on two axes. It helps examine association, nonlinearity, clusters and unusual pairs. Every point must represent a meaningful pair from the same case or aligned observation.

Expected output: A paired-variable plot with a context-aware description of association.

01 · Purpose

Why and when to use it

Use it to explore potential relationships and generate questions for Analyze, without treating association as proof of causation.

Who takes part?

The measurement owner works with people who understand the process and the recorded data. Statistical or measurement-system decisions should involve someone able to check the assumptions and study design.

02 · Application

How to apply it

Start with an agreed measurement purpose, explicit units and trustworthy observations. Identify missing cases and assumptions before calculating.

  1. Define two numerical variables and the pairing rule.
  2. Check units, missing pairs and time alignment.
  3. Plot each pair with labelled axes.
  4. Inspect shape, groups and influential observations.
  5. Consider confounding and time effects before choosing further analysis.

03 · Example

A worked measurement example

Original fictional teaching example. Small datasets illustrate the method; they are not proposed as sufficient study sizes. Supplied parameters are assumptions, not estimates from a real process.

OrderLines in orderPacking time (minutes)
A13
B35
C59
D710
Lines per orderPacking time (minutes)13570510
Paired order line counts and packing duration; association is not causation.

04 · Interpretation

What the result tells you

Packing time rises with line count in these four fictional pairs. The plot cannot establish that line count alone explains the difference; item size, staff and workload may also matter.

Completion check: A paired-variable plot with a context-aware description of association.

05 · Judgement

Mistakes and limitations

Pairing unrelated observations creates meaningless results. A weak linear pattern can conceal a curved relationship; grouping can change the apparent association.

07 · Reading

Sources and further reading

The tool list follows the accepted LeanTrainingLab overview and its reference to Six Sigma+Lean Toolset, second edition (Lunau, editor; Springer, 2013). The book chapters were not available for direct review during this build. Explanations, examples and worksheets are original teaching material. Further reading supplies professional background; local conventions are stated explicitly.