Control Charting Dependent Data: A Case Study

Dodson, Bryan

doi:10.1080/08982119508918822

Search citation statements

Order By: Relevance

Paper Sections

Select...

Autocorrelations Coefficients Thread's Resistance1

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2008

2012

Publication Types

Select...

Other3

Book1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…169,9 ,0000 169,2 ,0000 169,1 ,0000 168, 3 ,0000 165, 6 ,0000 163, 5 ,0000 160, 6 ,0000 159, 8 ,0000 159, 7 ,0000 150, 6 ,0000 149, 9 ,0000 146, 6 ,0000 143,2 ,0000 142, 9 ,0000 138, 5 , S.E. As we can see, the data are highly autocorrelated.…”

Section: Autocorrelations Coefficients Thread's Resistancementioning

confidence: 87%

Applications of Control Charts Arima for Autocorrelated Data

Russo¹,

Camargo²,

Fabris³

2012

Practical Concepts of Quality Control

View full text Add to dashboard Cite

Section: Autocorrelations Coefficients Thread's Resistancementioning

confidence: 87%

Applications of Control Charts Arima for Autocorrelated Data

Russo¹,

Camargo²,

Fabris³

2012

Practical Concepts of Quality Control

View full text Add to dashboard Cite

“…In this paper we present a general approach to analyzing autocorrelated data (Harris & Ross, 1991;Dobson, 1995;Montgomery, 2000;Ryan, 1989, Shewhart, 1931.…”

Section: Introductionmentioning

confidence: 99%

Statistical process control by model Bayesian

Camargo

Filho

Dullius

et al. 2008

2008 4th IEEE International Conference on Management of Innovation and Technology

View full text Add to dashboard Cite

Currently considerable attention has been given to the effect of data correlation on statistical process control (SPC). Use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. The objetive of this paper is to develop an algorithm to adjust a Dynamic Linear Model, to calculate the run length distribution (RLD), the average run length (ARL), standard deviation of the run length (SRL), for residual control charts X and R. The algorithm is applied to data collected from a textile company. The results showed that the process had been out of control needing systematic monitoring, with the objective of improving the quality of the products.

show abstract