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首页> 外文期刊>International Journal of Statistics and Probability >Application of Statistical Monitoring Using Autocorrelated Data and With the Influence of Multicollinearity in a Steel Process
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Application of Statistical Monitoring Using Autocorrelated Data and With the Influence of Multicollinearity in a Steel Process

机译：统计监测应用自相关数据及多元素在钢制过程中的影响

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The purpose of this article is to demonstrate a practical application of control charts in an industrial process that has data auto-correlated with each other. Although the control charts created by Walter A. Shewhart are very effective in monitoring processes, there are very important statistical assumptions for Shewhart's control charts to be applied correctly. Choosing the correct Control Chart is essential for managers to be able to make coherent decisions within companies. With this study, it was possible to demonstrate that the original data collected in the process, which at first appeared to have many special causes of variation, was actually a stable process (no anomalies present). However, this finding required the use of autoregressive models, multivariate statistics, autocorrelation and normality tests, multicollinearity analysis and the use of the EWMA control chart. It was concluded that it is of fundamental importance to choose the appropriate control chart for monitoring industrial processes, to ensure that changes in processes do not incorporate non-existent variations and do not cause an increase in the number of defective products.
机译：本文的目的是展示控制图在具有数据相互关联的数据的工业过程中的实际应用。虽然沃尔特A. Shewhart创建的控制图在监控过程中非常有效，但是对于正确应用的申请申请的控制图表非常重要的统计假设。选择正确的控制图表对于能够在公司内进行连贯的决策是必不可少的。通过这项研究，可以证明在该过程中收集的原始数据，它起初似乎具有许多特殊的变异原因，实际上是一种稳定的过程（没有存在的异常）。但是，这一发现要求使用自回归模型，多变量统计，自相关和正常测试，多型性分析和EWMA控制图的使用。它的结论是，为监测工业流程的适当控制图表是至关重要的，以确保过程的变化不包含不存在的变化，并且不会导致缺陷产品数量增加。

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