A Comparison of dispersion quality control charts
Kwami Tuprah, Matoteng M. Ncube
Abstract
Kwami Tuprah, Matoteng M. Ncube
Abstract
The problem of detecting shifts in the process variability has not received as much attention as that of detecting shifts in the process mean of continuous production processes, even though it is important in the context of quality control. We examine and compare shewhart s (1931) and Page s (1963) procedures for detecting shifts in variability based on the sample range (R-charts), with procedures based on the sample standard deviation (S-charts). The underlying process control variables are assumed to be normally distributed. We also compare Cumulative Sum (CUSUM) procedures using sample ranges and sample standard deviations. We shall show by average run length (ARL) comparisons that procedures based on the sample standard deviation detect shifts from target value more quickly than procedures based on the sample range.
OpenAlex reports 50 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The problem of detecting shifts in the process variability has not received as much attention as that of detecting shifts in the process mean of continuous production processes, even though it is important in the context of quality control. We examine and compare shewhart s (1931) and Page s (1963) procedures for detecting shifts in variability based on the sample range (R-charts), with procedures based on the sample standard deviation (S-charts). The underlying process control variables are assumed to be normally distributed. We also compare Cumulative Sum (CUSUM) procedures using sample ranges and sample standard deviations. We shall show by average run length (ARL) comparisons that procedures based on the sample standard deviation detect shifts from target value more quickly than procedures based on the sample range.
Key concepts: Mathematics, Statistics, Control chart, Dispersion (optics), Quality (philosophy), Econometrics, Computer science, Process (computing)