Statistical Performance of a Control Chart for Individual Observations Monitoring the Ratio of Two Normal Variables

Alireza Faraz

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


Statistical Process Control monitoring of the ratio Z of two normal variables X and Y has received too little attention in quality control literature. Several applications dealing with monitoring the ratio Z can be found in the industrial sector, when quality control of products consisting of several raw materials calls for monitoring their proportions (ratios) within a product. Tables about the statistical performance of these charts are still not available. This paper investigates the statistical performance of a Phase II Shewhart control chart monitoring the ratio of two normal variables in the case of individual observations. The obtained results show that the performance of the proposed chart is a function of the distribution parameters of the two normal variables. In particular, the Shewhart chart monitoring the ratio Z outperforms the (p = 2) multivariate T2control chart when a process shift affects the in-control mean of X or, alternatively, of Y and the correlation among X and Y is high and when the in-control means of X and Y shift contemporarily to opposite directions. The sensitivity of the proposed chart to a shift of the in-control dispersion has been investigated, too. We also show that the standardization of the two variables before computing their ratio is not a good practice due to a significant loss in the chart's statistical performance. An illustrative example from the food industry details the implementation of the ratio control chart.

Original languageEnglish
Pages (from-to)1361-1377
Number of pages17
JournalQuality and Reliability Engineering International
Issue number8
Publication statusPublished - 1 Dec 2014


  • Average run length
  • Ratio
  • Shewhart control chart
  • Standardization
  • Statistical Process Control


Dive into the research topics of 'Statistical Performance of a Control Chart for Individual Observations Monitoring the Ratio of Two Normal Variables'. Together they form a unique fingerprint.

Cite this