• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.811-823
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• 2018

Multivariate control chart based on Hotelling's $T^2$ statistic is a powerful tool in statistical process control for identifying an out-of-control process. It is used to monitor multiple process characteristics simultaneously. Detection of the out-of-control signal with the $T^2$ chart indicates mean vector shifts. However, these multivariate signals make it difficult to interpret the cause of the out-of-control signal. In this paper, we review methods of signal interpretation based on the Mason, Young, and Tracy (MYT) decomposition of the $T^2$ statistic. We also provide an example on how to implement it using R software and demonstrate simulation studies for comparing the performance of these methods.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.379-386
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• 2002

As the value and relative importance of service industry boosts in modern society, to strengthen competitiveness companies starts to recognize the quality of service as a key component in their business operations. Furthermore, companies that apply the quality of services as their competitive method are concentrating more on the area of quality control. Nevertheless, the previous researches related to this area were more focused on the aspect of measure rather than the control. In this paper, each fractionated customer group in consideration of its characteristics is made in use of estimating the quality of service using the conjoint analysis. The principal variable selective method which a small number of variables is used to control a large number of variables is applied. Hotelling's $T^2$ control chart is applied to administer many relative variables of the quality of service because it is a Multi-item. If abnormal indications appear by adding new data, the method to find and improve variables using MYT decomposition is suggested to apply.