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Optimal sample sizes are calculated from variance predictions by Ospats.
Optimal sample sizes for predicting the response are also derived.
Finally microstructure study has been performed for the optimal sample by using optical microscope.
The analysis demonstrates that this approach can be used to establish: optimal sample size; optimal sample allocation; and the societal payoff to proposed research.
Imprecision was evaluated based on event rate, optimal sample size, and width of confidence intervals.
The following notations will be used in later analysis: v † denotes the optimal sample path estimator.
Optimal sample sizes are derived for the case where the linearizing transformation is log(dose).
Optimal sample sizes for constructing tolerance limits are determined by minimizing appropriate cost functions.
It also allows here, prediction of optimal sample properties better than 8% from experimental values.
Porosity parameters and scanning electron microscopy were used to investigate the obtained optimal sample.
The sample of d and E which has minf is called the optimal sample.
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