We have developed and evaluated a new procedure for detecting trends in quality-control measurements and applied it to laboratory data. The method requires the use of sequential or "moving" slope estimates to identify trends. Formulae are derived to estimate the regression error for the moving slope directly from the standard deviation of the analytical measurements obtained during characteriza...

Traditional literature on statistical quality control discusses separately multivariate control charts for independent processes and univariate control charts for autocorrelated processes. We extend univariate residual monitoring to the multivariate environment, and propose using vector autoregressive residuals (VAR) to monitor multivariate processes in the presence of serial correlation. We ma...

The statistical performance of parametric control charts is questionable when the underlying process does not follow any specified probability distribution. Nonparametric are best substitute for this situation. On other hand, ranked set sampling technique preferred over simple random because it reduces variability parameters and improves chart’s performance. This study aims to offer a nonparame...

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...

The basic assumption of the parametric control charts is that underlying process follows specific distribution. appropriateness questionable when different industrial applications do not support this desired assumption. Recently, nonparametric have been introduced to overcome deficiency charts. Nonparametric same in-control run-length characteristics for all continuous distributions and are rob...