I fitted a random effects model with 2 factors (Part No. and Operator) by selecting the "Random Effects" option under "Attributes" on "Fit Model" window (with interaction Part*Operator).
I received the following output under REML Variance Components Estimates table.
Random Effect | Var Ratio | Var Component | Std Error | 95% Lower | 95% Upper | Wald p-Value | Pct of Total |
Part | 1.1555556 | 1.7333333 | 0.8656795 | 0.0366326 | 3.430034 | 0.0453* | 53.608 |
Operator | -0.004115 | -0.006173 | 0.0218 | -0.0489 | 0.0365544 | 0.7771 | 0.000 |
Part*Operator | -0.199588 | -0.299383 | 0.1464372 | -0.586394 | -0.012371 | 0.0409* | 0.000 |
Residual |
| 1.5 | 0.3354102 | 1.0110933 | 2.4556912 |
| 46.392 |
Total |
| 3.2333333 | 0.9283863 | 1.9759148 | 6.2317667 |
| 100.000 |
-2 LogLikelihood = 203.88510393
How to interpret these results?
The main result of REML is the Var Component column. The components from the model sum to the Total. The next result is the confidence interval or p-Value for the Wald ratio. The Part and Part*Operator estimates appear to be significantly different from zero. Model hierarchy requires keeping the Operator term even though it does not appear to be different from zero.
The negative estimate for the interaction component is because it is really a covariance, which can be negative. Another community member might have more insight in the interpretation in the case of MSA.
The Part variance is about 54% of the total and the residual (repeatability) is about 46% of the total, so this measurement system is not good.
Did you also analyze your data with the Variability Chart platform?
I'll add just a bit to @markbailey 's explanation by making sure that you ALSO plotted the data using a variety of the graphical techniques available in the Gauge R & R and/or Measurement Systems Analysis platforms and subplatforms. These charts can often times be very informative by visualizing features/characteristics in the measurement system that are of interest and valuable over and above the static tabular results that are also presented. Here's a link to the online documentation for the MSA platform as a good place to start:
https://www.jmp.com/support/help/14-2/measurement-systems-analysis.shtml#
And here's a JMP On Demand Mastering JMP webinar that I hosted during my SAS tenure that you might also find valuable:
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