Statistical quality control is the application of statistical methods to problems for which it is of interest to evaluate, establish, or verify the quality of a product. The two basic areas of statistical quality control that have received both the greatest attention in the literature and the widest acceptance in industry are acceptance sampling and statistical process control. In the majority of such techniques, a single characteristic of an item is used to describe its quality. In such cases, one of two basic types of product quality measures is typically used: attributes product quality measures and variables product quality measures. Variables product quality measures evaluate an item’s quality by measuring its quality characteristic on a continuous scale. Attributes product quality measures assign a 0 to an item if its characteristic is conforming to some specification, and 1 if its characteristic is nonconforming. Although attributes and variables product quality measures have many appropriate applications, there are many situations m which product quality is best described by classifying a single characteristic of the item using three or more discrete levels. A multilevel discrete product quality measure is a function that assigns a numerical value to such an item corresponding to the level in which it is classified. Several acceptance sampling plans and control charts that incorporate the use of multilevel discrete product quality measures are defined here. In addition to the multilevel discrete product quality measure, each of the defined methods utilizes a quality value function. A quality value function assigns a numerical value to an item based on the classification it receives from the multilevel discrete product quality measure. Each of the defined multilevel acceptance sampling plans and multilevel control charts is evaluated with respect to its probabilistic behavior. In addition, the problem of parameter selection and quality value function specification is addressed for each of the defined techniques. The cases considered are the 3-level case, the 4-level case, and the general j-level case. / Ph. D.

A análise de dados multidimensionais tem sido por muitos anos tópico de contínua investigação e uma das razões se deve ao fato desse tipo de dados ser encontrado em diversas áreas da ciência. Uma tarefa comum ao se analisar esse tipo de dados é a investigação de padrões pela interação em projeções multidimensionais dos dados para o espaço visual. O entendimento da relação entre as características do conjunto de dados (dataset) e a técnica utilizada para se obter uma representação visual desse dataset é de fundamental importância uma vez que esse entendimento pode fornecer uma melhor intuição a respeito do que se esperar da projeção. Por isso motivado, no presente trabalho investiga-se alguns aspectos de centralidade dos dados em dois cenários distintos: coleções de documentos com grafos de coautoria; dados multidimensionais mais gerais. No primeiro cenário, o dado multidimensional que representa os documentos possui informações mais específicas, o que possibilita a combinação de diferentes aspectos para analisá-los de forma sumarizada, bem como a noção de centralidade e relevância dentro da coleção. Isso é levado em consideração para propor uma metáfora visual combinada que possibilite a exploração de toda a coleção, bem como de documentos individuais. No segundo cenário, de dados multidimensionais gerais, assume-se que tais informações não estão disponíveis. Ainda assim, utilizando um conceito de estatística não-paramétrica, deno- minado funções de profundidade de dados (data-depth functions), é feita a avaliação da ação de técnicas de projeção multidimensionais sobre os dados, possibilitando entender como suas medidas de profundidade (centralidade) foram alteradas ao longo do processo, definindo uma também medida de qualidade para projeções. / Analysis of multidimensional data has been for many years a topic of continuous research and one of the reasons is such kind of data can be found on several different areas of science. A common task analyzing such data is to investigate patterns by interacting with spatializations of the data onto the visual space. Understanding the relation between underlying dataset characteristics and the technique used to provide a visual representation of such dataset is of fundamental importance since it can provide a better intuition on what to expect from the spatialization. Motivated by this, in this work we investigate some aspects of centrality on the data in two different scenarios: document collection with co-authorship graphs; general multidimensional data. In the first scenario, the multidimensional data which encodes the documents is much more information specific, meaning it makes possible to combine different aspects such as a summarized analysis, as well as the centrality and relevance notions among the documents in the collection. In order to propose a combined visual metaphor, this is taken into account make possible the visual exploration of the whole document collection as well as individual document analysis. In the second case, of general multidimensional data, there is an assumption that such additional information is not available. Nevertheless, using the concept of data-depth functions from non-parametric statistics it is analyzed the action of multidimensional projection techniques on the data, during the projection process, in order to make possible to understand how depth measures computed in the data have been modified along the process, which also defines a quality measure for multidimensional projections.

A análise de dados multidimensionais tem sido por muitos anos tópico de contínua investigação e uma das razões se deve ao fato desse tipo de dados ser encontrado em diversas áreas da ciência. Uma tarefa comum ao se analisar esse tipo de dados é a investigação de padrões pela interação em projeções multidimensionais dos dados para o espaço visual. O entendimento da relação entre as características do conjunto de dados (dataset) e a técnica utilizada para se obter uma representação visual desse dataset é de fundamental importância uma vez que esse entendimento pode fornecer uma melhor intuição a respeito do que se esperar da projeção. Por isso motivado, no presente trabalho investiga-se alguns aspectos de centralidade dos dados em dois cenários distintos: coleções de documentos com grafos de coautoria; dados multidimensionais mais gerais. No primeiro cenário, o dado multidimensional que representa os documentos possui informações mais específicas, o que possibilita a combinação de diferentes aspectos para analisá-los de forma sumarizada, bem como a noção de centralidade e relevância dentro da coleção. Isso é levado em consideração para propor uma metáfora visual combinada que possibilite a exploração de toda a coleção, bem como de documentos individuais. No segundo cenário, de dados multidimensionais gerais, assume-se que tais informações não estão disponíveis. Ainda assim, utilizando um conceito de estatística não-paramétrica, deno- minado funções de profundidade de dados (data-depth functions), é feita a avaliação da ação de técnicas de projeção multidimensionais sobre os dados, possibilitando entender como suas medidas de profundidade (centralidade) foram alteradas ao longo do processo, definindo uma também medida de qualidade para projeções. / Analysis of multidimensional data has been for many years a topic of continuous research and one of the reasons is such kind of data can be found on several different areas of science. A common task analyzing such data is to investigate patterns by interacting with spatializations of the data onto the visual space. Understanding the relation between underlying dataset characteristics and the technique used to provide a visual representation of such dataset is of fundamental importance since it can provide a better intuition on what to expect from the spatialization. Motivated by this, in this work we investigate some aspects of centrality on the data in two different scenarios: document collection with co-authorship graphs; general multidimensional data. In the first scenario, the multidimensional data which encodes the documents is much more information specific, meaning it makes possible to combine different aspects such as a summarized analysis, as well as the centrality and relevance notions among the documents in the collection. In order to propose a combined visual metaphor, this is taken into account make possible the visual exploration of the whole document collection as well as individual document analysis. In the second case, of general multidimensional data, there is an assumption that such additional information is not available. Nevertheless, using the concept of data-depth functions from non-parametric statistics it is analyzed the action of multidimensional projection techniques on the data, during the projection process, in order to make possible to understand how depth measures computed in the data have been modified along the process, which also defines a quality measure for multidimensional projections.

PURPOSE: Substantial research demonstrates that many nursing homes are not providing adequate quality of care for residents because of inadequate processes of care and insufficient numbers and training of nursing staff. Some residents experience poor-quality outcomes, such as pressure ulcers and unintended weight loss. To address these issues, the long-term care industry has focused on improving quality of care by improving staff education about care processes and reducing nurse turnover rate. The aim of this study was to compare the association between - licensed nurse hours of care and turnover rate to five quality measures—incidence of ADL decline, physical restraints, prevalence of pressure ulcers among residents with risk factors for skin breakdown, and prevalence of unintended weight loss. DESIGN: The study population included 618 freestanding Texas nursing facilities. A cross-sectional multiple regression analysis was conducted using 2007 data to examine the relationship between nurse staffing levels and turnover and resident outcomes with control variables for facility and resident characteristics. RESULTS: The high-risk pressure ulcer variable accounted for 10% of the variation in the regression model. LVN (hprd) was positively associated with an increase in high-risk pressure ulcers. The ADL decline variable accounted for 7.5% of the variation in the regression model, and RN hprd was positively associated with ADL decline. The prevalence of weight loss variable accounted for 3% of the variation in the regression model. RN turnover rate was approaching significance. The physical restraint variable accounted for 2% of the variation in the regression model. LVN contract hprd was significant and positively associated with an increase in physical restraint use although a minimal contribution to the model given the low percentage of LVN contract hprd. The prevalence of low-risk pressure ulcers was not significant. The control variable case mix index was positively associated with ADL decline, high-risk pressure ulcers, and weight loss. CONCLUSIONS: This research will contribute to understanding the relationship of licensed nursing staff to resident outcomes. Additionally, it will contribute to nursing education, research, and policy. While controlling for acuity using case mix index, some negative findings were still noted that requires further research. / text