Constructing Hotelling’s T2 control limits for different α levels of significance
AbstractWhen the quality of a product is controlled by combinations of two or more variables, a high level decision qui-ckly determines the out of control signals, while simultaneously, evaluating the variable(s) responsible for the false alarm rate (α) levels of significance. In this connection, bootstrap algorithm for constructing Hotelling’s T2 control limits was developed and implemented using Visual Basic Code to resolve such problem. The results obtained using industrial production process data proved that the bootstrap control limit at different α levels of significance performed better when compared with other existing methods. Akaike Information Criterion was also applied to identify the variable(s) responsible for the out of control signals.
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