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Measure · Tool guide 38 of 40

Data Transformation

A data transformation applies a defined mathematical function to measurements, for example y = ln(x) for positive values. It can make a model’s assumptions more reasonable or reveal structure. It changes the analysis scale, not the physical process.

Expected output: A documented scale transformation with a justified analysis and interpretable results.

01 · Purpose

Why and when to use it

Consider it when a justified statistical analysis needs a different scale; also consider fitting an appropriate original-scale distribution instead.

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. Clarify the analysis purpose and inspect data validity and stability.
  2. Choose a transformation whose domain covers the observations.
  3. Apply it consistently and record the function and parameters.
  4. Check model assumptions again on the transformed scale.
  5. Transform relevant specifications consistently and explain results in original units where appropriate.

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.

Duration x (minutes)ln(x)
10
20.693
41.386
82.079

04 · Interpretation

What the result tells you

Equal doubling of duration becomes equal spacing on a log scale. exp(mean(log x)) is the geometric mean, not the original arithmetic mean.

Completion check: A documented scale transformation with a justified analysis and interpretable results.

05 · Judgement

Mistakes and limitations

Logarithms require positive values; do not silently add a constant to accommodate zeros. Transformation does not fix bad measurement, dependence, mixtures or instability.

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.