Gráficos de controle para dados do tipo taxas e proporções autocorrelacionados

Detalhes bibliográficos
Ano de defesa: 2016
Autor(a) principal: Tondolo, Cátia Michele
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Santa Maria
BR
Engenharia de Produção
UFSM
Programa de Pós-Graduação em Engenharia de Produção
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
β
Link de acesso: http://repositorio.ufsm.br/handle/1/8392
Resumo: This work discusses one of the areas for quality improvement, defined as statistical process control (SPC). One of the most used tools in SPC is the control chart, which is used to monitor parameters of a process. In general, these charts are built under normality and independence assumptions of observations. However, sometimes these suppositions do not occur. The usual control charts work reasonably well if normal distribution assumption is moderately violated, but the violation of independence assumption reduces the applicability of them. When the data are autocorrelated, it is adequate to use residuals control charts, usually from ARIMA class. The residuals are used to produce the usual control charts, like the Shewhart, CUSUM and EWMA. In addition, variables restricted to the interval (0,1), such as rates and proportions, are naturally assumed to follow beta distribution. Thus, we propose the use of control chart with different residuals of the model βARMA to model and monitor autocorrelated beta distributed processes. The performance measures of the proposed control charts were evaluated by Monte Carlo simulations and the ARL (average run length) was analyzed under control and out of control. Proposed and traditional models were compared for autocorrelated data adjustment. Two applications were performed using real data associated to the volume of energy stored in southern Brazil and the levels of the sources of the Cantareira System (São Paulo, Brazil). The proposed control charts showed good performance for rates and proportions data, getting a better detection of special causes than usual modeling.