Open AccessInvestigation of Multivariate Statistical Process Control in R Enviroment
Author(s) -
József Mihalkó,
Róbert Rajkó
Publication year - 2017
Publication title -
analecta
Language(s) - English
Resource type - Journals
ISSN - 2064-7964
DOI - 10.14232/analecta.2017.2.36-40
Subject(s) - univariate , principal component analysis , multivariate statistics , statistical process control , process (computing) , multivariate analysis , computer science , principal (computer security) , decomposition , control (management) , statistics , data mining , artificial intelligence , mathematics , machine learning , chemistry , organic chemistry , operating system
At the first stage of our work, the theoretical knowledge needed to use the multivariate statistical process control (MSPC) was explored. Last year, we clarified the sometimes confused concepts, equations, and formulas [1]. At the second stage, R project simulation studies and some food industrial practical model investigations are carried out for confirming the MSPC advantages compared with the univariate ones. Furthermore, we analyse, using principal component analysis (PCA), what could cause the outlying values. Moreover, we will demonstrate how to use the MYT-decomposition.