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2. Measurement Process Characterization
2.4. Gauge R & R studies
2.4.4. Analysis of variability

2.4.4.3.

Analysis of stability

Case study: Resistivity probes Run-to-run variability can be assessed graphically by a plot of check standard values (averaged over J repetitions) versus time with a separate graph for each check standard. Data on all check standards should be plotted on one page to obtain an overall view of the measurement situation.
Advantage of pooling A level-3 standard deviation with (L - 1) degrees of freedom is computed from the run averages. Because there will rarely be more than two runs per check standard, resulting in one degree of freedom per check standard, it is prudent to have three or more check standards in the design to take advantage of pooling. The mechanism for pooling over check standards is shown in the table below. The pooled standard deviation has
Q(L - 1) degrees and is shown as the last entry in the right-hand column of the table.
Example of pooling The following table shows how the level-3 standard deviations for a single gauge (probe #2362) are pooled over check standards. The table can be reproduced using
R code.

    Level-3 standard deviations for a single gauge pooled over check standards
    Source of variability
    Standard deviation
    Degrees of freedom
    Sum of squares
    Level-3
    \( {\large s}_{3i} \)
    \( \nu_i\)
    \( SS_{i} = v_{i} \cdot {s_{3i}}^{2} \)
    
    Chk std 138
    
    Chk std 139
    
    Chk std 140
    
    Chk std 141
    
    Chk std 142
    
    Sum
    
    
    Pooled value
    \( s_{3} = \sqrt{ \sum{SS_{i}} / \sum{v_{i}} } \)
    0.0223 0.0027 0.0289 0.0133 0.0205
    1 1 1 1 1 ----- 5
    0.0004973 0.0000073 0.0008352 0.0001769 0.0004203 ----------- 0.0019370 0.0197
Level-3 standard deviations A subset of data collected in a nested design on one check standard (#140) with probe (#2362) for six days and two runs is analyzed for between-run effects. The level-3 standard deviation, computed from the averages of two runs, is 0.02885 with one degree of freedom. Dataplot code and R code can be used to perform the calculations for this data.
Relationship to long-term changes, days and gauge precision The size of the between-run effect can be calculated by subtraction using the standard deviations for days and gauge precision as $$ {\large s}_{runs} = \sqrt{{\large s}_3^2 - \frac{1}{K} {\large s}_2^2} = \sqrt{{\large s}_3^2 - \frac{1}{k} {\large s}_{days}^2 - \frac{1}{KJ} {\large s}_1^2} $$
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