Each tab in the Constant Stress ALT Design platform Test Plans section summarizes the plan test settings and model, provides three test plans, and provides diagnostics to help select between test plans.
Test Settings and Acceleration Model
The table provides a summary of the plan specifications that define the test plans that are contained in the tab.
For each set of Constant Stress ALT plan specifications, three test plans are generated. An alert appears when a plan is not capable of estimating the model parameters. The three test plans to consider for your test are the optimal, compromise, and balanced plans.
Optimal Plan
A plan built to optimize the large-sample approximate variance of the optimality criterion specified in the plan settings. Compared to the compromise and balanced plans, the optimal plan typically has the fewest test settings. However, it is also the most dependent on the assumptions of the underlying model.
Note: Optimal designs are provided as a reference against which to compare the compromise and balanced plans. The optimal design might have too few test settings for estimating all of the acceleration model parameters.
Compromise Plan
A plan built to optimize the large-sample variance of the optimality criterion specified in the plan settings with the requirement of a middle test setting. This setting is between the lowest and highest settings with a minimum proportion of test units allocated to the setting. The default for the minimum proportion is 10%. Use the Set Minimum Proportion option in the Plan Comparisons red triangle menu to change the proportion.
Balanced Plan
A plan built to provide every combination of test settings, based on the number of levels per factor. Compared to the optimal and compromise plans, the balanced plan typically has the most test settings with an equal distribution of test units across settings.
The Constant Stress ALT Design platform Diagnostics section has a table of metrics for each plan type.
Approx. Std Dev of Log Quantile
The approximate standard deviation of the logarithm of the quantile estimate. It is computed using a large-sample approximation based on the Fisher information.
Approx. Std Dev of Log Failure Probability
The approximate standard deviation of the logarithm of the failure probability. It is computed using a large-sample approximation based on the Fisher information.
R Precision Factor
The R Precision Factor (for a 95% CI) is a measure of the precision of the 95% confidence interval for the quantile of interest. A value less than or equal one is optimal.
The CSALT diagnostics section provides three diagnostic plots.
Distribution Profiler
A profiler to evaluate the impact of plan type and factor(s) on the failure probabilities. The probabilities are based on the model assumptions.
R Precision Factor for a 95% CI
A profiler to evaluate the impact of plan type, number of units on test, level of the factor(s), and the quantile of interest on the R Precision Factor.
Model Sensitivity Profiler
A profiler to evaluate the impact of plan type, number of units on test, and failure time estimates for the settings specified in the Acceleration Model Settings on the large-sample approximate variance of the metric of interest. Above this profiler, there is a reference table summarizing the factor levels and quantile specified in the Acceleration Model Settings for each setting. Use this profiler to evaluate how sensitive the approximate variance is to failure time assumptions.