A synopsis of the importance and the nature of reading serve as the point of departure for this study. The pupils involved are learning restrained as well as A comprehensive reading problem analysis table, compiled for analysis of individual reading problems, facilitates identification of the remedial reading areas, as well as the underlying subskills causing the problems to be accommodated in remedial reading. A control chart, developed for recording the information concerning the pupil's reading problem area and underlying subskills, facilitates compilation of an integrated remedial reading programme. Existing exercises, selected from the works of various authors and adapted, provide guidelines and exercises for particular remedial reading areas. These guidelines serve as a point of departure for the compilation of a specific remedial reading programme for a particular pupil with reading problems. Two case studies elucidate how a remedial reading programme can be compiled according to the pupil's background, reading problems and inadequacies in the underlying subskills. Group A learning disabled pupils although learning disabled pupils in Group B and C can also be involved. The total reading process is illustrated by means of a reading model. The two main components, namely, word identification and comprehension, form the basis of this study. The different subcategories featuring in each component are highlighted. This model serves as a framework for the diagnosis and remediation of reading problems. A teaching model is used to illustrate the complexity of teaching. The factors ( within the teaching model are indicated, as well as the ways they may serve when reading is taught. The reduction and choice of reading content for a specific pupil are set out as important aspects to be taken into consideration in reading remediation. Determining each pupil's reading levels, namely, his/her independent level, instructional level and frustrational level, enables the teacher to choose the appropriate reading material. / Teacher Education / D. Ed. (Orthopedagogics)

[pt] Os gráficos de controle estão entre as ferramentas indispensáveis para monitorar o desempenho de um processo em várias indústrias. Quando estimativas de parâmetros são necessárias para projetar esses gráficos, seu desempenho é afetado devido aos erros de estimação. Para resolver esse problema, no passado, pesquisadores avaliavam o desempenho desses métodos em termos do valor esperado do número médio de amostras até um alarme falso condicionado às estimativas dos parâmetros (denotado por 𝐶𝐴𝑅𝐿0). No entanto, esta solução não considera a grande variabilidade do 𝐶𝐴𝑅𝐿0 entre usuários. Então, recentemente, surgiu a ideia de medir o desempenho dos gráficos de controle usando a probabilidade de o 𝐶𝐴𝑅𝐿0 ser maior do que um valor especificado – que deve estar próximo do desejado nominal. Isso é chamado de Exceedance Probability Criterion (EPC). Para aplicar o EPC, a função de distribuição acumulada (c.d.f.) do 𝐶𝐴𝑅𝐿0 é necessária. No entanto, para um dos gráficos de controle mais utilizados, o gráfico Xbarra, também conhecido como gráfico x (sob a suposição de distribuição normal), a expressão matemática da c.d.f. não está disponível na literatura. Como contribuição nesse sentido, o presente trabalho apresenta a derivação exata da expressão matemática da c.d.f. do 𝐶𝐴𝑅𝐿0 para três possíveis casos de estimação de parâmetros: (1) quando a média e o desvio-padrão são desconhecidos, (2) quando apenas a média é desconhecida e (3) quando apenas o desvio-padrão é desconhecido. Assim, foi possível calcular o número mínimo de amostras iniciais, m, que garantem um desempenho desejada do gráfico em termos de EPC. Esses resultados mostram que m pode assumir valores consideravelmente grandes (como, por exemplo, 3.000 amostras). Como solução, duas novas equações são derivadas aqui para ajustar os limites de controle garantindo assim um desempenho desejado para qualquer valor de m. A vantagem dessas equações é que uma delas fornece resultados exatos enquanto a outra dispensa avançados softwares de computador para os cálculos. Um estudo adicional sobre o impacto desses ajustes no desempenho fora de controle (OOC) fornece tabelas que ajudam na decisão do melhor tradeoff entre quantidade adequada de dados e desempenhos IC e OOC preferenciais do gráfico. Recomendações práticas para uso desses resultados são aqui também fornecidas. / [en] Control charts are among the indispensable tools for monitoring process performance in various industries. When parameter estimation is needed to design these charts, their performance is affected due to parameter estimation errors. To overcome this problem, in the past, researchers have evaluated the performance of control charts and designed them in terms of the expectation of the realized in-control (IC) average run length (𝐶𝐴𝑅𝐿0). But, as pointed recently, this solution does not account for what is known as the practitioner-to-practitioner variability (i.e., the variability of 𝐶𝐴𝑅𝐿0). So, a recent idea emerged where control chart performance is measured by the probability of the 𝐶𝐴𝑅𝐿0 being greater than a specified value - which must be close to the nominal desired one. This is called the Exceedance Probability Criterion (EPC). To apply the EPC, the cumulative distribution function (c.d.f.) of the 𝐶𝐴𝑅𝐿0 is required. However, for the most well-known control chart, named the two-sided Shewhart Xbar (or simply X) Chart (under normality assumption), the mathematical c.d.f. expression of the 𝐶𝐴𝑅𝐿0 is not available in the literature. As a contribution in this respect, the present work presents the derivation of the exact c.d.f. expression of the 𝐶𝐴𝑅𝐿0 for three cases of parameters estimation: (1) when both the process mean and standard deviation are unknown, (2) when only the mean is unknown and (3) when only the standard deviation is unknown. Using these key results, it was possible to calculate the exact minimum number of initial (Phase I) samples (m) that guarantees a desired in-control performance in terms of the EPC. These results show that m can be prohibitively large (such as 3.000 samples). As a solution to this problem, two new equations are derived here to adjust the control limits to guarantee a desired in-control performance in terms of the EPC for any given value of m. The advantage of these equations (compared to the existing adjustments methods) is that one provides exact results and the other one does not require too many computational resources to perform the calculations. A further study about the impact of these adjustments on the out-of-control (OOC) performance provides useful tables to decide the appropriate amount of data and the adjustments that corresponds to a user preferred tradeoff between the IC and OOC performances of the chart. Practical recommendations for using these findings are also provided in this research work.

This thesis deals with the evaluation of the control system outputs in the production of fire car. In the first part of thesis is evaluated control system by company THT, Ltd.. The second, theoretical part, describes the seven basic quality tools, which represents tools for detection and analyzing of quality problems. In the third, practical part, is describes the proposed program, which serves as a simple tool for identification of the most frequent disagreements in the fire engines. In the final part is evaluated implementation of the proposed program in the company.

Statistical process control (SPC) applies the science of statistics to various process control in order to provide higher-quality products and better services. The K chart is one among the many important tools that SPC offers. Creation of the K chart is based on Support Vector Data Description (SVDD), a popular data classifier method inspired by Support Vector Machine (SVM). As any methods associated with SVM, SVDD benefits from a wide variety of choices of kernel, which determines the effectiveness of the whole model. Among the most popular choices is the Euclidean distance-based Gaussian kernel, which enables SVDD to obtain a flexible data description, thus enhances its overall predictive capability. This thesis explores an even more robust approach by incorporating the Mahalanobis distance-based kernel (hereinafter referred to as Mahalanobis kernel) to SVDD and compare it with SVDD using the traditional Gaussian kernel. Method's sensitivity is benchmarked by Average Run Lengths obtained from multiple Monte Carlo simulations. Data of such simulations are generated from multivariate normal, multivariate Student's (t), and multivariate gamma populations using R, a popular software environment for statistical computing. One case study is also discussed using a real data set received from Halberg Chronobiology Center. Compared to Gaussian kernel, Mahalanobis kernel makes SVDD and thus the K chart significantly more sensitive to shifts in mean vector, and also in covariance matrix.

Diese Dissertation konzentriert sich auf das hochdimensionale Financial Engineering, insbesondere in der Dependenzmodellierung und der sequentiellen Überwachung. Im Bereich der Dependenzmodellierung wird eine Einführung hochdimensionaler Kopula vorgestellt, die sich auf den Stand der Forschung in Kopula konzentriert. Eine komplexere Anwendung im Financial Engineering, bei der eine hochdimensionale Kopula verwendet wird, konzentriert sich auf die Bepreisung von Portfolio-ähnlichen Kreditderivaten, d. h. CDX-Tranchen (Credit Default Swap Index). In diesem Teil wird die konvexe Kombination von Kopulas in der CDX-Tranche mit Komponenten aus der elliptischen Kopula-Familie (Gaussian und Student-t), archimedischer Kopula-Familie (Frank, Gumbel, Clayton und Joe) und hierarchischer archimedischer Kopula-Familie vorgeschlagen. Im Abschnitt über finanzielle Überwachung konzentriert sich das Kapitel auf die Überwachung von hochdimensionalen Portfolios (in den Dimensionen 5, 29 und 90) durch die Entwicklung eines nichtparametrischen multivariaten statistischen Prozesssteuerungsdiagramms, d.h. eines Energietest-basierten Kontrolldiagramms (ETCC). Um die weitere Forschung und Praxis der nichtparametrischen multivariaten statistischen Prozesskontrolle zu unterstützen, die in dieser Dissertation entwickelt wurde, wird ein R-Paket "EnergyOnlineCPM" entwickelt. Dieses Paket wurde im Moment akzeptiert und veröffentlicht im Comprehensive R Archive Network (CRAN), welches das erste Paket ist, das die Verschiebung von Mittelwert und Kovarianz online überwachen kann. / This dissertation focuses on the high dimensional financial engineering, especially in dependence modeling and sequential surveillance. In aspect of dependence modeling, an introduction of high dimensional copula concentrating on state-of-the-art research in copula is presented. A more complex application in financial engineering using high dimensional copula is concentrated on the pricing of the portfolio-like credit derivative, i.e. credit default swap index (CDX) tranches. In this part, the convex combination of copulas is proposed in CDX tranche pricing with components stemming from elliptical copula family (Gaussian and Student-t), Archimedean copula family (Frank, Gumbel, Clayton and Joe) and hierarchical Archimedean copula family used in some publications. In financial surveillance part, the chapter focuses on the monitoring of high dimensional portfolios (in 5, 29 and 90 dimensions) by development of a nonparametric multivariate statistical process control chart, i.e. energy test based control chart (ETCC). In order to support the further research and practice of nonparametric multivariate statistical process control chart devised in this dissertation, an R package "EnergyOnlineCPM" is developed. At moment, this package has been accepted and published in the Comprehensive R Archive Network (CRAN), which is the first package that can online monitor the shift in mean and covariance jointly.

O monitoramento da razão Z de duas variáveis através de gráfico de controle tem sido um tema recentemente explorado na literatura. Para analisar mais o assunto, o estudo avalia a eficiência e viabilidade de aplicação dessa ferramenta como suporte na tomada de decisão para gerenciamento de capacidade de mão de obra de retaguarda (doravante mencionado como Backoffice) de serviço de empresa do setor bancário. Tradicionalmente, gráficos de controle tem sido utilizados para monitorar o processo produtivo de manufaturas, mas recentemente tem sido adotado para monitoramento de alguns serviços. Apesar de ainda seguir muitos conceitos pioneiros na manufatura, a atividade do setor de serviços apresenta suas particularidades como, por exemplo, a impossibilidade de gerar estoque. Assim, a necessidade de adequar seus recursos à demanda torna-se essencial, sendo fundamental a gestão de controles e sua urgência para que possa reagir rapidamente em caso de variação de demanda e adequar sua capacidade. Em um cenário de restrição de recursos, planejar é crucial para evitar desperdícios e garantir eficiência. O objetivo deste estudo é apresentar o gráfico de controle como ferramenta para monitorar a razão de duas variáveis aleatórias: a demanda e a mão de obra em Backoffice de serviço em um banco. Nesse trabalho, gráfico de controle de Shewhart tradicional e gráfico de controle de Shewhart com regras suplementares são analisados e os resultados obtidos confirmam a possibilidade de utilização da ferramenta de gráficos de controle para o gerenciamento e adequação de mão de obra para atender a demanda. O monitoramento da razão (demanda/ mão de obra) ajudará o gestor a alocar adequadamente o time (mão de obra) de acordo com a demanda e a capacidade produtiva. Como contribuição, o estudo avalia o comportamento da razão Z = X/Y em situação de alta variabilidade da variável X e baixa variabilidade da variável Y . / The monitoring of the ratio Z of two variables through control chart has been recently explored in the literature. This study analyses the efficiency and viability of this tool as a support to the manager\'s decision in capacity management of a Backoffice service in a business banking service. Traditionally, control charts have been used to monitoring production process in manufacturing companies, but recently it has been adopted in monitoring some classes of services. Although many pioneering definitions developed for manufacturing are still applied in the service activities, the service industry has some particularities as the impossibility to generate stock, for example. Thus, the need to adapt the resources of manpower to attend the demand becomes essential mainly in a current financial global crisis, with resource constraints. The control management is fundamental and urgent to plan a fast reaction to adequate its capacity in case of a large demand variation. Thus, monitoring is crucial to avoid vast and ensure efficiency. The aim of this paper is to present a control chart to monitor the ratio of the two random quantities: the demand and the labor of a Backoffice service in a business banking service. In this paper, traditional Shewhart and Shewhart with supplementary rules control charts are considered and the results confirmed the possibility to apply the control charts to help the management to adequate the manpower in order to attend the demand. The monitoring of the ratio (demand/manpower) will help the manager to allocate adequately the team (the manpower) according to the demand and the production capacity. The contribution of this study is the analysis of the behavior of the ratio Z = X/Y in situation of large variability of the variable X and low variability of the variable Y .

近幾年來，有一些研究提出了只用單一一個管制圖即可同時偵測平均數和變異數。根據此目的，我們提出了加權平均損失管制圖，此管制圖是利用加權平均損失所建立的，在一個製成的目標值和平均數不一定相等時，它可同時監控一個製成的平均數和變異數。此加權平均損失統計量是應用一個加權因子，去調整製程平均和目標值的平方差和變異數的損失比重，所以此管制圖的效能比未經由加權因子調整過的管制圖還好。我們不只建立了固定管制參數(FP)加權平均損失管制圖，也建立了適應性加權平均損失管制圖，包括變動抽樣間隔(VSI)、變動樣本數與抽樣間隔(VSI)、變動管制參數(VP)；我們利用平均連串長度(ARL)來衡量固定管制參數管制圖的偵測績效，利用馬可夫鏈的方法計算偵測出異常訊息所需的平均時間(ATS)來衡量適應性管制圖的績效，並且做比較，我們發現適應性管制圖比固定管制參數管制圖的效能還要好。我們也利用最佳化技術建立最加適應性管制圖，當製成失控時，此最佳化管制圖能使ATS1最小。此外，當平均數和變異數的偏移幅度很小時，我們利用指數加權移動平均法(EWMA)建立EWMA加權平均損失管制圖，使其有較好的偵測力。這些我們所提出的管制圖，是只根據單一一個統計量所建立的，和X bar-S管制圖相比，有較好的效能，且和使用兩個管制圖同時偵測平均數和變異數相比，比較輕易理解且容易執行。 / In recent years, a few researchers had proposed different types of single charts that jointly monitor the process mean and the variation. In this project, we use the weighted average loss (WL) to construct WL control charts for monitoring the process mean and variance simultaneously while the target value may be different from the in-control mean. This statistic WL applied a weighted factor to adjust the weights of the loss due to the square of the deviation of the process mean from the target and the variance change. So the WL charts are more effective than unadjusted loss function charts. We not only construct the fixed parameters (FP) WL chart but also the adaptive WL charts which included variable sampling interval (VSI) WL chart, variable sample size and sampling interval (VSSI) WL chart and variable parameters (VP) WL chart. We calculate the average run length (ARL) for FP WL chart and using Markov chain approach to calculate the average time to signal (ATS) for adaptive WL charts to measure the performance and compare each other. From the comparison, we find the adaptive WL charts are more effective than the FP WL chart. We also proposed the optimal adaptive WL charts using an optimization technique to minimize ATS1 (ARL1) when the process was out-of-control. In addition, in order to detect the small shifts of the process mean and variance effectively, we construct the WL charts using the EWMA scheme. The proposed charts are based on only one statistic and are more effective than the X bar-S chart. And the WL charts are easy to understand and apply than using two charts for detecting the mean and variance shifts simultaneously.

This article proposes the algorithm of a new control chart (loss function control chart) based on the Taguchi loss function with an adaptive scheme for binomial data. The loss function control chart is able to monitor cost variation from the process by applying loss function in the design. This new angle economically explores production cost. This research provides designs of the loss function control chart with specified VSI, optimal VSI, VSS and VP, respectively. Numerical analyses show that the specified VSI loss function chart, the optimal VSI loss function chart, the optimal VSS loss function chart and the optimal VP loss function chart outperform the Fp loss function chart significantly and show costs can be controlled systematically.

O monitoramento da razão Z de duas variáveis através de gráfico de controle tem sido um tema recentemente explorado na literatura. Para analisar mais o assunto, o estudo avalia a eficiência e viabilidade de aplicação dessa ferramenta como suporte na tomada de decisão para gerenciamento de capacidade de mão de obra de retaguarda (doravante mencionado como Backoffice) de serviço de empresa do setor bancário. Tradicionalmente, gráficos de controle tem sido utilizados para monitorar o processo produtivo de manufaturas, mas recentemente tem sido adotado para monitoramento de alguns serviços. Apesar de ainda seguir muitos conceitos pioneiros na manufatura, a atividade do setor de serviços apresenta suas particularidades como, por exemplo, a impossibilidade de gerar estoque. Assim, a necessidade de adequar seus recursos à demanda torna-se essencial, sendo fundamental a gestão de controles e sua urgência para que possa reagir rapidamente em caso de variação de demanda e adequar sua capacidade. Em um cenário de restrição de recursos, planejar é crucial para evitar desperdícios e garantir eficiência. O objetivo deste estudo é apresentar o gráfico de controle como ferramenta para monitorar a razão de duas variáveis aleatórias: a demanda e a mão de obra em Backoffice de serviço em um banco. Nesse trabalho, gráfico de controle de Shewhart tradicional e gráfico de controle de Shewhart com regras suplementares são analisados e os resultados obtidos confirmam a possibilidade de utilização da ferramenta de gráficos de controle para o gerenciamento e adequação de mão de obra para atender a demanda. O monitoramento da razão (demanda/ mão de obra) ajudará o gestor a alocar adequadamente o time (mão de obra) de acordo com a demanda e a capacidade produtiva. Como contribuição, o estudo avalia o comportamento da razão Z = X/Y em situação de alta variabilidade da variável X e baixa variabilidade da variável Y . / The monitoring of the ratio Z of two variables through control chart has been recently explored in the literature. This study analyses the efficiency and viability of this tool as a support to the manager\'s decision in capacity management of a Backoffice service in a business banking service. Traditionally, control charts have been used to monitoring production process in manufacturing companies, but recently it has been adopted in monitoring some classes of services. Although many pioneering definitions developed for manufacturing are still applied in the service activities, the service industry has some particularities as the impossibility to generate stock, for example. Thus, the need to adapt the resources of manpower to attend the demand becomes essential mainly in a current financial global crisis, with resource constraints. The control management is fundamental and urgent to plan a fast reaction to adequate its capacity in case of a large demand variation. Thus, monitoring is crucial to avoid vast and ensure efficiency. The aim of this paper is to present a control chart to monitor the ratio of the two random quantities: the demand and the labor of a Backoffice service in a business banking service. In this paper, traditional Shewhart and Shewhart with supplementary rules control charts are considered and the results confirmed the possibility to apply the control charts to help the management to adequate the manpower in order to attend the demand. The monitoring of the ratio (demand/manpower) will help the manager to allocate adequately the team (the manpower) according to the demand and the production capacity. The contribution of this study is the analysis of the behavior of the ratio Z = X/Y in situation of large variability of the variable X and low variability of the variable Y .

Successful organizations continually seek ways to improve productivity, reduce and control costs, and increase efficiency. Governmental entities also are driven by the need for increased efficiency and accountability in public service for their constituents.There is a continuing need for better tools and a number of government entities have turned to performance management systems due to their promise of improvement in various areas of productivity and accountability. This research focused on one such system, Six Sigma, which has recently experienced widespread adoption in industry in the United States, internationally, and in some government organizations. In this study Six Sigma was compared and contrasted with several performance management systems, and its effects and organizational cultural impacts on one organization were examined.The study investigated the financial implications and perceptions of organizational cultural change resulting from the Six Sigma system implementation in a large government enterprise. The first part of the study used the organization’s published financial information from 1997 through 2006 to determine whether there was a tangible financial benefit of implementing Six Sigma. The analysis indicated that the financial implications were statistically significant and quantified them as material and relevant to the organization’s two major business units.The second component of the research explored differences in organizational culture and attitudes among and between selected employee groups through the use of interviews and a survey instrument. Interviews were also conducted with a purposive sample of the executives who were involved in the decisions to implement Six Sigma. The Organizational Culture Inventory© and Organizational Effectiveness Inventory™ survey instruments were used to measure the organizational culture perceptions of the employee groups. Discriminant function analysis results suggested that the various groups shared a common organizational culture, which supports the null hypothesis that there were no differences in the organizational cultural perceptions among the organizational groups investigated.