All diagnostics in the Design Diagnostics report are based on simulations that are computed from the sampling distributions of the appropriate mean squares. You can specify a simulation distribution and the parameter values for the distribution for each model factor. The design diagnostics update with changes to the settings. This enables you to explore the ability of your design to measure your systems under different assumed models. Update the number of levels in your design to explore alternatives.
The Variance Component Estimator Performance report enables you to specify a simulation distribution and parameter values for each MSA model factor. These values are used in a large number of simulation trials to provide estimates of what you might observe in your study given your design and simulation assumptions. Select between variance components or variance proportions estimates.
The Variance Component Estimator Performance report contains the following elements:
Simulation Distribution
The simulation distribution for each model term. You can select between a fixed effect, random uniform, random normal, or random gamma.
Distribution Parameters
The parameters for the specified distribution.
Variance Component
The name of the model term.
Estimate Plot
Plot of the estimate and the range of the simulated residuals. Green intervals indicate that the term is included in the model. Red intervals indicate a term that is not in the model.
Tip: Click the intervals to include or exclude terms from the model.
Estimated Bias
The average bias observed in the simulation trials. Each simulation has a true and an estimated variance component. The bias for each trial is the residual.
Estimated Std Err
The average standard error of the residuals observed in the simulation trials.
The Variance Component Estimator Performance red triangle menu contains the following option:
Make Data Table
Opens a data table of the simulation trials.The table contains the simulated true value and the estimated value for each trial.
A Gauge R&R report based on the simulated trials. Use to estimate the accuracy and sampling uncertainty that you can expect in these particular metrics based on the particular design.
An EMP report based on the simulated trials. For more information about EMP classification seeMonitor Classification Legend in Quality and Process Methods.