Solved Examples
Crossed Gage R&R — MSA Type 2 qualityTools teaching dataset
Worked example Open teaching dataset Not your current analysis

MSA Type 2 · Crossed Gage R&R · ANOVA

Separating repeatability, reproducibility and part variation

Follow a balanced crossed study in which three operators measure the same ten parts twice. The example shows how ANOVA separates sources of variation and how the resulting components are read.

1

Study design

Every operator measures every part

The source vignette describes ten randomly selected parts and three operators. Each operator measures each part twice, with parts presented in a randomized, blinded order. The published response vector is artificial and was created for teaching, so it does not reproduce proprietary customer or VDA QMC data.

DesignCrossed
Operators3
Parts10
Repeats per cell2
Observations60
AnalysisTwo-way ANOVA
Why “crossed” matters: operators A, B and C all measure the same physical parts. This makes operator, part and operator-by-part effects estimable in one model.
2

Input preparation

Long-form data that QualityFirst can map directly

CSV preview

OperatorPartRepeatMeasurement
A1123
A1220
A2122
A2224
A3123
A3221

Preview: six of 60 rows. The Repeat column is traceability information.

Column mapping

Operator
Appraiser identifier; map to Operator.
Part
Common part identifier; map to Part.
Repeat
Measurement occasion; retained for traceability.
Measurement
Numeric response; map to Measurement.
QualityFirst settings Method: ANOVA · Interaction α: 0.25 · Tolerance: blank

The source does not define specification limits, so this example does not calculate %Tolerance. No acceptance limits are entered.

ANOVA

Statistical model

Four possible sources of observed variation

Part, operator, their interaction and repeatability are separated before the Gage R&R components are assembled.

σ²Total = σ²Part + σ²Operator + σ²Part×Operator + σ²Repeatability

Repeatability

Variation between repeated readings made under the same part–operator combination. In the ANOVA model it is represented by residual variation.

Reproducibility

Variation associated with operators and, when retained, the way operator differences depend on the measured part.

3

Calculated result

Interaction decision and variance components

ANOVA with interaction

SourceDFSSMSFp
Operator220.6310.3178.5970.00112
Part9107.0711.8969.9147.31×10−7
Operator × Part1822.031.2241.0200.46732
Residual3036.001.200——

Why the reduced model is used

The operator-by-part interaction gives p = 0.46732. With the displayed QualityFirst setting α = 0.25, it is not retained. Its variation is pooled into the residual and the final components come from the model without interaction.

The interaction decision is a model-selection step. It is not an acceptance decision about the measurement system.

Operator by part interaction plot and components of study variation
Reproduction from the published values. The left plot shows mean readings by part and operator; the right plot shows the components on the %Study Variation scale.

Final reduced-model components

SourceVariance componentStandard deviation6 × SD%Study Variation
Total Gage R&R1.6641.2907.7469.5%
Repeatability1.2091.1006.6059.2%
Reproducibility0.4550.6754.0536.4%
Part-to-Part1.7811.3358.0171.9%
Total Variation3.4461.85611.14100.0%
Distinct categoriesndc = 1

QualityFirst reproduces the published reduced-model variance components to rounding. Ratios such as %Study Variation are calculated from standard deviations, not by adding the displayed percentages.

4

Interpretation

What each result contributes to the investigation

  • The non-retained interaction means the final model does not estimate a separate Part × Operator variance component.
  • Repeatability is the larger measurement-system component in this dataset.
  • The operator means differ; operator C has the highest overall mean reading.
  • Part-to-part spread and measurement-system spread both contribute materially to total observed variation.
  • ndc summarizes separation relative to measurement-system variation; it does not identify the physical cause.
  • Suitability still depends on the measurement task, tolerance, risk and applicable customer requirements.
No pass/fail label is assigned here. This page explains a published teaching calculation. It intentionally does not apply AIAG, VDA, OEM or customer-specific acceptance thresholds.
5

Reproducibility

Sources and licence