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ABSTRACT Process capability indices (PCIs) play a fundamental role in assessing and quantifying the capability of manufacturing processes to meet customer specifications. To date, the majority of PCIs that are now in use have been examined through the use of novel generalizations of PCIs with symmetric tolerances to check the performance of an industrial manufacturing process. In this paper, we proposed new estimations of some indices with asymmetric tolerances in the presence of outliers. New robust and parametric estimators of some PCIs are introduced to estimate and compare these indices for each normal distribution with the presence of different outliers. Meanwhile, this paper discusses how well the proposed method can be used for non‐normal data. For illustration purpose, the application example is presented. [ABSTRACT FROM AUTHOR]

Study aim: The purpose of this study was to investigate the role of sleep and awareness on consolidation of general and Sequence-Specific learning in children.

Hypotheses testing is an effective technique for making a decision about the process capability. The adversity to test C p k based on determining the distribution of its natural estimator is very complex, even under the Normal distribution. Thus, the Monte Carlo simulation approach is an executable procedure for the hypotheses testing of C p k based on the natural estimator. The proposed approach is a testing technique to evaluate whether products quality meets the preset specification limits. This procedure implies a Monte Carlo statistical test, which is applicable to Normal processes. A case study in an automobile factory is presented to check out the products, and numerical computations are presented to show the effect of the Monte Carlo critical values for making a reliable decision on the hypotheses testing of C p k. [ABSTRACT FROM AUTHOR]