T² Hotelling Control Chart with Bootstrap Method for Paper Production Quality Control

A multivariate control chart is a control chart used when there is more than one quality characteristic in an inspection. Hotellingcontrol chart is one of the statistical tools used to monitor multivariate shifts in process mean using the mean vector and covariance matrix. Hotelling control chart with the bootstrap method represents an alternative approach to dealing with multivariate non-normal data. This approach does not require specific assumptions such as the multivariate normal assumption, which are typically required by other methods. This study uses the bootstrap method with a different algorithm, the aim is to find the smallest ARL value. The method was applied to secondary data on quality characteristics of paper pulp with three variables: pH, moisture content, and brightness. The results show that the bootstrap control chart on the statistical value has an ARL value of 6.24 while the bootstrap control chart on the observation sample has an ARL value of 8.33.

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Publication Details

Journal
Journal of Applied Statistics and Data Science.
Published
2024-10-28
DOI
https://doi.org/10.21776/ub.jasds.2024.001.02.1
Primary Topic
Advanced Statistical Process Monitoring
Type
article
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article

T² Hotelling Control Chart with Bootstrap Method for Paper Production Quality Control

Alya Ratna Sandria
Journal of Applied Statistics and Data Science.
Advanced Statistical Process Monitoring
article

T² Hotelling Control Chart with Bootstrap Method for Paper Production Quality Control

Alya Ratna Sandria
article en

Abstract

A multivariate control chart is a control chart used when there is more than one quality characteristic in an inspection. Hotellingcontrol chart is one of the statistical tools used to monitor multivariate shifts in process mean using the mean vector and covariance matrix. Hotelling control chart with the bootstrap method represents an alternative approach to dealing with multivariate non-normal data. This approach does not require specific assumptions such as the multivariate normal assumption, which are typically required by other methods. This study uses the bootstrap method with a different algorithm, the aim is to find the smallest ARL value. The method was applied to secondary data on quality characteristics of paper pulp with three variables: pH, moisture content, and brightness. The results show that the bootstrap control chart on the statistical value has an ARL value of 6.24 while the bootstrap control chart on the observation sample has an ARL value of 8.33.

Journal of Applied Statistics and Data Science.Vol. 1(2)
University of Brawijaya (ID)
Openalex Percentile: Top 81%
Advanced Statistical Process Monitoring
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