Relevant bibliographies by topics / Quality control – Statistical methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Quality control – Statistical methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Quality control – Statistical methods"

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Nagata, Yasushi. "Statistical Methods for Quality Control." Seikei-Kakou 31, no. 4 (March 20, 2019): 132–36. http://dx.doi.org/10.4325/seikeikakou.31.132.

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Зенкін, Микола Анатолійович. "Print Quality Control Using Statistical Methods." Технологія і техніка друкарства, no. 3(69) (November 10, 2020): 52–58. http://dx.doi.org/10.20535/2077-7264.3(69).2020.217390.

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Розглянуто можливість використання статистичних методів для забезпечення виробництва продукції, яка відповідає вимогам споживачів з найменшими витратами. Виконано аналіз різних досліджень, що дозволило виявити споживчі вимоги до кінцевої продукції. Описано принципи та практичні методи Загального менеджменту якості TQM. Відмічено, що суворий контроль, який покликаний забезпечити максимальну для друкованого виробництва якість, не обмежений одним калібрувальним тестовим тиражом — результати друку необхідно перевіряти постійно. Встановлено, що у стандарті ISО 12647-2:2004 відсутні: оптимальні значення густини для трьох типів паперу; значення CIELАB для балансу по сірому; параметри паперу конкретних виробників; параметри кольорів конкретних виробників; параметри пластин конкретних виробників; специфічні добавки та інші допоміжні матеріали для друку. Стандарт визначає вимірювані результати, на які потрібно вийти, але не дає методики та рекомендацій з їх досягнення. Запропоновано можливість застосування методу побудови контрольної карти для виявлення причин відхилення показників оптичної густини для тріади фарб в офсетному друці. З’ясовано, що найбільш важливими вимогами споживачів є розрізнення дрібних деталей зображення, тексту, чіткість друку, відсутність плям і сторонніх елементів на зображенні, естетичність. Розроблені в роботі підходи дозволяють визначити найбільш слабке місце в системі папір (картон)—друкарська фарба і оцінити рівень якості.
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Prístavka, Miroslav, and Marián Bujna. "Use of Statistical Methods in Quality Control." Acta Technologica Agriculturae 16, no. 2 (June 1, 2013): 35–38. http://dx.doi.org/10.2478/ata-2013-0009.

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Abstract More and more organisations are beginning to realise how important is it to implement a quality management system. Its main task is to rejuvenate the given processes in production. Problem solving is systemic within quality assurance procedures of an organisation. This work describes a quality management system according to ISO 9001 and statistical methods in quality management. The theoretical part contains the characteristics and description of the system listed above. The practical part shows the use of knowledge in the organisation to solve problems.
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Nembhard, Harriet Black. "Statistical Process Adjustment Methods for Quality Control." Journal of the American Statistical Association 99, no. 466 (June 2004): 567–68. http://dx.doi.org/10.1198/jasa.2004.s340.

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Rizkina, Manisyah, Machrani Adi Putri Siregar, and Ana Uzla Batubara. "Deli River Water Quality Control In Medan City Using Statistical Methods Quality Control." Jurnal Pijar Mipa 19, no. 2 (March 30, 2024): 372–79. http://dx.doi.org/10.29303/jpm.v19i2.6591.

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Clean water is a type of water-based resource that is of good quality and is commonly used by humans for consumption or in carrying out daily activities. Clean water quality that meets standards/quality is very difficult to obtain because river water quality has been polluted by various kinds of waste from various human activities, so that the potential impact on river quality decreases both in terms of quantity and quality. 70% of the Deli River pollution is solid and liquid waste, domestic waste, industrial waste, and along the Deli River it has affected the quality of the river water. To quantitatively identify air quality, the Statistical Quality Control method can be used. Statistical quality control is a collection of strategies, techniques, and actions taken to ensure that they produce a quality product. The aim of this research is to determine the control of the water quality of the Deli River in Medan City using Statistical Quality Control. Based on the data obtained, the water quality standards of the Deli River in the Sumatra II River Basin Agency from January 2023 - December 2023 have not been statistically controlled because there are several data samples that are out of control. Then, Deli River water quality control was carried out based on graphic control using SQC (Statistical Quality Control). The results of quality control using SQC show that SQC provides different controls, because in SQC TDS control on data on controlled 3 times, while the and R data on DO, the and R data on and and R data for fatty oil are controlled.
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Yankovich, E., O. Nevidimova, and K. Yankovich. "Methods of Statistical Control for Groundwater Quality Indicators." IOP Conference Series: Materials Science and Engineering 132 (June 2016): 012019. http://dx.doi.org/10.1088/1757-899x/132/1/012019.

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Paksy, L. "Use of mathematical-statistical methods in spectrochemical quality control." Microchemical Journal 45, no. 3 (June 1992): 318–28. http://dx.doi.org/10.1016/0026-265x(92)90091-g.

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Parfentsevа, N. О., and H. V. Holubova. "Statistical Methods for Quality Control: A Tool for Data Analysis in the Statistica Package." Statistics of Ukraine 100, no. 1 (March 31, 2023): 9–26. http://dx.doi.org/10.31767/su.1(100)2023.01.02.

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The article substantiates the applicability of statistical methods for product quality assessment, analysis of production processes, business processes, etc. The notion “quality” is characterized and its properties are defined: suitability, operational efficiency, appropriate content, etc. It is highlighted that the State Standard of Ukraine is a national body for standardization, metrology and certification, which defines and approves the quality standardization system in accordance with the international standards Guidance on statistical techniques for ISO 9001:2000. The focus is made on the main methods for quality control, which are considered the most relevant and most widely used. The application of seven quality control methods is described in detail: Control Sheets, Pareto Diagram, Stratification, Histogram, Scatter Diagram, Cause and Effect Diagram, Control Chart. It is substantiated that in a digitalized economy with large scopes of accumulated information, the use of statistical data processing packages is an indisputable tool for analysts. Using statistical quality control methods implemented in the Statistica package, the authors conducted research on simulated data and constructed appropriate graphs and charts. Pareto diagram is designed for ranking the factors with impact on a production process or product quality. The stratification method allows for performing a variance analysis, to determine each factor’s effect on the result. The main advantage of the histogram method is its visibility and simplicity for analyzing the homogeneity of a distribution and checking for normality. Scatter diagrams allow one to evaluate the correlation strength and make graphical descriptions of the dependence between production factors, reveal the impact of a factor characteristic on the resulting one, etc. The Ishikawa’s cause-and-effect diagram provides a tool for arranging the factors with effect on the production process. The use of control charts makes enables for analyzing the production process in dynamics. It is emphasized that the described quality control methods can be applied in any sequence, production cycle or combination: altogether or as separate analytical tools. Based on the results of the study, the main challenges faced now by business analysts are summed up: the mastery of statistical tools and computer data processing for making effective management decisions.
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van Delft, Christian. "Some New Classroom Cases for Teaching Statistical Quality Control Methods." Quality Engineering 14, no. 1 (January 2002): 45–48. http://dx.doi.org/10.1081/qen-100106885.

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Stanley, James D. "Statistical Methods for Industrial Process Control." Journal of Quality Technology 30, no. 3 (July 1998): 303–5. http://dx.doi.org/10.1080/00224065.1998.11979862.

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Dissertations / Theses on the topic "Quality control – Statistical methods"

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Barr, Tina Jordan. "Performance of quality control procedures when monitoring correlated processes." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/25497.

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Ritchie, Paul Andrew 1960. "A systematic, experimental methodology for design optimization." Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276698.

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Much attention has been directed at off-line quality control techniques in recent literature. This study is a refinement of and an enhancement to one technique, the Taguchi Method, for determining the optimum setting of design parameters in a product or process. In place of the signal-to-noise ratio, the mean square error (MSE) for each quality characteristic of interest is used. Polynomial models describing mean response and variance are fit to the observed data using statistical methods. The settings for the design parameters are determined by minimizing a statistical model. The model uses a multicriterion objective consisting of the MSE for each quality characteristic of interest. Minimum bias central composite designs are used during the data collection step to determine the settings of the parameters where observations are to be taken. Included is the development of minimum bias designs for various cases. A detailed example is given.
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Jamnarnwej, Panisuan. "Methods for detection of small process shifts." Diss., Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/24518.

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Ghebretensae, Manna Zerai. "A unified approach to the economic aspects of statistical quality control and improvement." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/49865.

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Assignment (MSc)--Stellenbosch University, 2004.
ENGLISH ABSTRACT: The design of control charts refers to the selection of the parameters implied, including the sample size n, control limit width parameter k, and the sampling interval h. The design of the X -control chart that is based on economic as well as statistical considerations is presently one of the more popular subjects of research. Two assumptions are considered in the development and use of the economic or economic statistical models. These assumptions are potentially critical. It is assumed that the time between process shifts can be modelled by means of the exponential distribution. It is further assumed that there is only one assignable cause. Based on these assumptions, economic or economic statistical models are derived using a total cost function per unit time as proposed by a unified approach of the Lorenzen and Vance model (1986). In this approach the relationship between the three control chart parameters as well as the three types of costs are expressed in the total cost function. The optimal parameters are usually obtained by the minimization of the expected total cost per unit time. Nevertheless, few practitioners have tried to optimize the design of their X -control charts. One reason for this is that the cost models and their associated optimization techniques are often too complex and difficult for practitioners to understand and apply. However, a user-friendly Excel program has been developed in this paper and the numerical examples illustrated are executed on this program. The optimization procedure is easy-to-use, easy-to-understand, and easy-to-access. Moreover, the proposed procedure also obtains exact optimal design values in contrast to the approximate designs developed by Duncan (1956) and other subsequent researchers. Numerical examples are presented of both the economic and the economic statistical designs of the X -control chart in order to illustrate the working of the proposed Excel optimal procedure. Based on the Excel optimization procedure, the results of the economic statistical design are compared to those of a pure economic model. It is shown that the economic statistical designs lead to wider control limits and smaller sampling intervals than the economic designs. Furthermore, even if they are more costly than the economic design they do guarantee output of better quality, while keeping the number of false alarm searches at a minimum. It also leads to low process variability. These properties are the direct result of the requirement that the economic statistical design must assure a satisfactory statistical performance. Additionally, extensive sensitivity studies are performed on the economic and economic statistical designs to investigate the effect of the input parameters and the effects of varying the bounds on, a, 1-f3 , the average time-to-signal, ATS as well as the expected shift size t5 on the minimum expected cost loss as well as the three control chart decision variables. The analyses show that cost is relatively insensitive to improvement in the type I and type II error rates, but highly sensitive to changes in smaller bounds on ATS as well as extremely sensitive for smaller shift levels, t5 . Note: expressions like economic design, economic statistical design, loss cost and assignable cause may seen linguistically and syntactically strange, but are borrowed from and used according the known literature on the subject.
AFRIKAANSE OPSOMMING: Die ontwerp van kontrolekaarte verwys na die seleksie van die parameters geïmpliseer, insluitende die steekproefgrootte n , kontrole limiete interval parameter k , en die steekproefmterval h. Die ontwerp van die X -kontrolekaart, gebaseer op ekonomiese sowel as statistiese oorwegings, is tans een van die meer populêre onderwerpe van navorsing. Twee aannames word in ag geneem in die ontwikkeling en gebruik van die ekonomiese en ekonomies statistiese modelle. Hierdie aannames is potensieel krities. Dit word aanvaar dat die tyd tussen prosesverskuiwings deur die eksponensiaalverdeling gemodelleer kan word. Daar word ook verder aangeneem dat daar slegs een oorsaak kan wees vir 'n verskuiwing, of te wel 'n aanwysbare oorsaak (assignable cause). Gebaseer op hierdie aannames word ekonomies en ekonomies statistiese modelle afgelei deur gebruik te maak van 'n totale kostefunksie per tydseenheid soos voorgestel deur deur 'n verenigende (unified) benadering van die Lorenzen en Vance-model (1986). In hierdie benadering word die verband tussen die drie kontrole parameters sowel as die drie tipes koste in die totale kostefunksie uiteengesit. Die optimale parameters word gewoonlik gevind deur die minirnering van die verwagte totale koste per tydseenheid. Desnieteenstaande het slegs 'n minderheid van praktisyns tot nou toe probeer om die ontwerp van hulle X -kontrolekaarte te optimeer. Een rede hiervoor is dat die kosternodelle en hulle geassosieerde optimeringstegnieke té kompleks en moeilik is vir die praktisyns om te verstaan en toe te pas. 'n Gebruikersvriendelike Excelprogram is egter hier ontwikkel en die numeriese voorbeelde wat vir illustrasie doeleindes getoon word, is op hierdie program uitgevoer. Die optimeringsprosedure is maklik om te gebruik, maklik om te verstaan en die sagteware is geredelik beskikbaar. Wat meer is, is dat die voorgestelde prosedure eksakte optimale ontwerp waardes bereken in teenstelling tot die benaderde ontwerpe van Duncan (1956) en navorsers na hom. Numeriese voorbeelde word verskaf van beide die ekonomiese en ekonomies statistiese ontwerpe vir die X -kontrolekaart om die werking van die voorgestelde Excel optimale prosedure te illustreer. Die resultate van die ekonomies statistiese ontwerp word vergelyk met dié van die suiwer ekomomiese model met behulp van die Excel optimerings-prosedure. Daar word aangetoon dat die ekonomiese statistiese ontwerpe tot wyer kontrole limiete en kleiner steekproefmtervalle lei as die ekonomiese ontwerpe. Al lei die ekonomies statistiese ontwerp tot ietwat hoër koste as die ekonomiese ontwerpe se oplossings, waarborg dit beter kwaliteit terwyl dit die aantal vals seine tot 'n minimum beperk. Hierbenewens lei dit ook tot kleiner prosesvartasie. Hierdie eienskappe is die direkte resultaat van die vereiste dat die ekonomies statistiese ontwerp aan sekere statistiese vereistes moet voldoen. Verder is uitgebreide sensitiwiteitsondersoeke op die ekonomies en ekonomies statistiese ontwerpe gedoen om die effek van die inset parameters sowel as van variërende grense op a, 1- f3 , die gemiddelde tyd-tot-sein, ATS sowel as die verskuiwingsgrootte 8 op die minimum verwagte kosteverlies sowel as die drie kontrolekaart besluitnemingsveranderlikes te bepaal. Die analises toon dat die totale koste relatief onsensitief is tot verbeterings in die tipe I en die tipe II fout koerse, maar dat dit hoogs sensitief is vir wysigings in die onderste grens op ATS sowel as besonder sensitief vir klein verskuiwingsvlakke, 8. Let op: Die uitdrukkings ekonomiese ontwerp (economic design), ekonomies statistiese ontwerp (economic statistical design), verlies kostefunksie (loss cost function) en aanwysbare oorsaak (assignable cause) mag taalkundig en sintakties vreemd voordoen, maar is geleen uit, en word so gebruik in die bekende literatuur oor hierdie onderwerp.
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Assareh, Hassan. "Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/53342/1/Hassan_Assareh_Thesis.pdf.

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Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.
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Ismail, Noor Azina. "Statistical methods for the improvement of health care." Thesis, Queensland University of Technology, 1999.

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Murphy, Terrence Edward. "Multivariate Quality Control Using Loss-Scaled Principal Components." Diss., Available online, Georgia Institute of Technology, 2004:, 2004. http://etd.gatech.edu/theses/available/etd-11222004-122326/unrestricted/murphy%5Fterrence%5Fe%5F200412%5Fphd.pdf.

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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2005.
Victoria Chen, Committee Co-Chair ; Kwok Tsui, Committee Chair ; Janet Allen, Committee Member ; David Goldsman, Committee Member ; Roshan Vengazhiyil, Committee Member. Vita. Includes bibliographical references.
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Harvey, Martha M. (Martha Mattern). "The Fixed v. Variable Sampling Interval Shewhart X-Bar Control Chart in the Presence of Positively Autocorrelated Data." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278763/.

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This study uses simulation to examine differences between fixed sampling interval (FSI) and variable sampling interval (VSI) Shewhart X-bar control charts for processes that produce positively autocorrelated data. The influence of sample size (1 and 5), autocorrelation parameter, shift in process mean, and length of time between samples is investigated by comparing average time (ATS) and average number of samples (ANSS) to produce an out of control signal for FSI and VSI Shewhart X-bar charts. These comparisons are conducted in two ways: control chart limits pre-set at ±3σ_x / √n and limits computed from the sampling process. Proper interpretation of the Shewhart X-bar chart requires the assumption that observations are statistically independent; however, process data are often autocorrelated over time. Results of this study indicate that increasing the time between samples decreases the effect of positive autocorrelation between samples. Thus, with sufficient time between samples the assumption of independence is essentially not violated. Samples of size 5 produce a faster signal than samples of size 1 with both the FSI and VSI Shewhart X-bar chart when positive autocorrelation is present. However, samples of size 5 require the same time when the data are independent, indicating that this effect is a result of autocorrelation. This research determined that the VSI Shewhart X-bar chart signals increasingly faster than the corresponding FSI chart as the shift in the process mean increases. If the process is likely to exhibit a large shift in the mean, then the VSI technique is recommended. But the faster signaling time of the VSI chart is undesirable when the process is operating on target. However, if the control limits are estimated from process samples, results show that when the process is in control the ARL for the FSI and the ANSS for the VSI are approximately the same, and exceed the expected value when the limits are fixed.
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Grayson, James M. (James Morris). "Economic Statistical Design of Inverse Gaussian Distribution Control Charts." Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc332397/.

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Statistical quality control (SQC) is one technique companies are using in the development of a Total Quality Management (TQM) culture. Shewhart control charts, a widely used SQC tool, rely on an underlying normal distribution of the data. Often data are skewed. The inverse Gaussian distribution is a probability distribution that is wellsuited to handling skewed data. This analysis develops models and a set of tools usable by practitioners for the constrained economic statistical design of control charts for inverse Gaussian distribution process centrality and process dispersion. The use of this methodology is illustrated by the design of an x-bar chart and a V chart for an inverse Gaussian distributed process.
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Sandholm, Thomas. "Statistical Methods for Computational Markets : Proportional Share Market Prediction and Admission Control." Doctoral thesis, KTH, Data- och systemvetenskap, DSV, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4738.

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We design, implement and evaluate statistical methods for managing uncertainty when consuming and provisioning resources in a federated computational market. To enable efficient allocation of resources in this environment, providers need to know consumers' risk preferences, and the expected future demand. The guarantee levels to offer thus depend on techniques to forecast future usage and to accurately capture and model uncertainties. Our main contribution in this thesis is threefold; first, we evaluate a set of techniques to forecast demand in computational markets; second, we design a scalable method which captures a succinct summary of usage statistics and allows consumers to express risk preferences; and finally we propose a method for providers to set resource prices and determine guarantee levels to offer. The methods employed are based on fundamental concepts in probability theory, and are thus easy to implement, as well as to analyze and evaluate. The key component of our solution is a predictor that dynamically constructs approximations of the price probability density and quantile functions for arbitrary resources in a computational market. Because highly fluctuating and skewed demand is common in these markets, it is difficult to accurately and automatically construct representations of arbitrary demand distributions. We discovered that a technique based on the Chebyshev inequality and empirical prediction bounds, which estimates worst case bounds on deviations from the mean given a variance, provided the most reliable forecasts for a set of representative high performance and shared cluster workload traces. We further show how these forecasts can help the consumers determine how much to spend given a risk preference and how providers can offer admission control services with different guarantee levels given a recent history of resource prices.
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Books on the topic "Quality control – Statistical methods"

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Ryan, Thomas P. Statistical methods for quality improvement. 2nd ed. New York: Wiley, 2000.

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S, Stephens Kenneth, and Godfrey A. Blanton, eds. Modern methods for quality control and improvement. 2nd ed. New York: Wiley, 2002.

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Wadsworth, Harrison M. Modern methods for quality control and improvement. New York: Wiley, 1986.

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Wadsworth, Harrison M. Modern methods for quality control and improvement. New York: Wiley, 1986.

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Standardization, International Organization for, ed. Statistical methods for quality control. 4th ed. Genève: ISO Central Secretariat, 1995.

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1908-, Burr Irving W., ed. Elementary statistical quality control. 2nd ed. New York: M. Dekker, 2005.

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S, Leavenworth Richard, ed. Statistical quality control. 6th ed. New York: McGraw-Hill, 1988.

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S, Leavenworth Richard, ed. Statistical quality control. 7th ed. New York: McGraw-Hill Co. Inc., 1996.

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Derman, Cyrus. Statistical aspects of quality control. San Diego: Academic Press, 1997.

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Doty, Leonard A. Statistical process control. 2nd ed. New York: Industrial Press, 1996.

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Book chapters on the topic "Quality control – Statistical methods"

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Selvamuthu, Dharmaraja, and Dipayan Das. "Statistical Quality Control." In Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control, 353–98. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1736-1_10.

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Jarrett, Jeffrey E. "Multivariate statistical quality control." In Mathematical and Statistical Methods in Food Science and Technology, 419–30. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118434635.ch21.

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Dyer, M. "Software Development Under Statistical Quality Control." In Software System Design Methods, 81–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-82846-1_4.

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Rayat, Charan Singh. "Statistical Quality Control in Clinical Laboratories." In Statistical Methods in Medical Research, 127–38. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0827-7_14.

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Momeni, Amir, Matthew Pincus, and Jenny Libien. "Statistical Concepts in Laboratory Quality Control." In Introduction to Statistical Methods in Pathology, 243–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60543-2_11.

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Srivastava, M. S. "Robustness of On-line Control Procedures." In Quality Improvement Through Statistical Methods, 97–108. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1776-3_8.

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Hubbard, Merton R. "Test Methods." In Statistical Quality Control for the Food Industry, 151–55. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0149-7_6.

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Hubbard, Merton R. "Test Methods." In Statistical Quality Control for the Food Industry, 147–52. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-2677-7_6.

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Hubbard, Merton R. "Test Methods." In Statistical Quality Control for the Food Industry, 130–34. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-1197-4_6.

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Hubbard, Merton R. "Test Methods." In Statistical Quality Control for the Food Industry, 147–52. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4684-0041-0_6.

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Conference papers on the topic "Quality control – Statistical methods"

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Bakhouche, M., F. Cointault, and P. Gouton. "Texture analysis with statistical methods for wheat ear extraction." In Eigth International Conference on Quality Control by Artificial Vision, edited by David Fofi and Fabrice Meriaudeau. SPIE, 2007. http://dx.doi.org/10.1117/12.736913.

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Рахматуллаев, Сарвар, and Малохат Ахмедова. "MANAGING PRODUCT QUALITY IN MANUFACTURING ENTERPRISES ANALYSIS OF STATISTICAL CONTROL METHODS." In Status and development trends of standardization and technical regulation in the world. Tashkent state technical university, 2022. http://dx.doi.org/10.51346/tstu-conf.22.1-77-0034.

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This paper discusses some static methods for analyzing product quality control in large industrial establishments. The analysis of statistic methods of quality control and the impact of the use of these methods on the efficiency of production in the quality control of manufactured products is carried out.
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Pavlickova, Marcela, and Marek Laciak. "Application of Statistical Methods in Improving the Quality of Bogie Frame." In 2022 23rd International Carpathian Control Conference (ICCC). IEEE, 2022. http://dx.doi.org/10.1109/iccc54292.2022.9805912.

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Razmochaeva, Natalya V., Viktor P. Semenov, and Artem A. Bezrukov. "Methods of Automating Processes in Statistical Control and Quality Management Field." In 2019 International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS). IEEE, 2019. http://dx.doi.org/10.1109/itqmis.2019.8928441.

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Nazaroh. "Statistical methods for quality control of dose calibrator in nuclear medicine." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE AND SCHOOL ON PHYSICS IN MEDICINE AND BIOSYSTEM (ICSPMB): Physics Contribution in Medicine and Biomedical Applications. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0048082.

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"Statistical Approaches to Quality Control in Concrete Manufacturing." In International Conference on Cutting-Edge Developments in Engineering Technology and Science. ICCDETS, 2024. http://dx.doi.org/10.62919/kkjh9731.

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This research paper explores the application of statistical methods in quality control for concrete manufacturing, aiming to enhance the consistency and reliability of concrete products. The study focuses on various statistical tools and techniques, such as Statistical Process Control (SPC), control charts, and capability analysis, which are employed to monitor and improve the concrete manufacturing process. By implementing these statistical approaches, manufacturers can better understand process variations, detect potential problems early, and implement corrective actions efficiently. The paper discusses the principles behind these techniques, their practical applications, and the benefits they offer in terms of improved product quality and reduced production costs. Through case studies and real-world examples, the paper demonstrates how these methods contribute to achieving higher standards in concrete manufacturing.
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Sembiring, Nurhayati, Elisabeth Ginting, and Sudikse Ingrid. "Quality Control on Flour Rice with Statistical Quality Control (SQC) Method and Taguchi Method." In International Conference of Science, Technology, Engineering, Environmental and Ramification Researches. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0010084402670272.

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"Welding Quality Control Using Statistical Quality Control (SQC) Methods and Failure Mode Effect Analysis (FMEA) at PT. XYZ." In 3rd International Conference Eco-Innovation in Science, Engineering, and Technology. Galaxy Science, 2022. http://dx.doi.org/10.11594/nstp.2022.2707.

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Baba, Yukino. "Statistical Quality Control for Human Computation and Crowdsourcing." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/806.

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Human computation is a method for solving difficult problems by combining humans and computers. Quality control is a critical issue in human computation because it relies on a large number of participants (i.e., crowds) and there is an uncertainty about their reliability. A solution for this issue is to leverage the power of the "wisdom of crowds"; for example, we can aggregate the outputs of multiple participants or ask a participant to check the output of another participant to improve its quality. In this paper, we review several statistical approaches for controlling the quality of outputs from crowds.
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Hirotsu, Chihiro. "Statistical training of researchers in total quality management: The Japanese experience." In Training Researchers in the Use if Statistics. International Association for Statistical Education, 2000. http://dx.doi.org/10.52041/srap.00103.

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A training system for statistical methods in Total Quality Control or Total Quality Management is discussed and we suggest what and how to teach. It is stated that we have no department of statistics in the universities in Japan and stressed that applied statistics is most efficiently taught to those who have their own problems and motivations to apply these statistical methods. It is then essential for a company to have their own training systems for the TQM researchers although some extra company training courses may also be efficiently utilised. As an example we introduce in some detail the seminars provided by JUSE as well as in-company training systems of Toyota Motor Corporation and Takenaka Corporation.
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Reports on the topic "Quality control – Statistical methods"

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Luo, Jing, Chen Zhang, Xiaoping Wu, and Rongrong Ren. The effect of acupoint catgut embedding and drug therapy on hyperlipidemia: a meta-analysis of randomized controlled trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0019.

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Review question / Objective: Acupoint catgut embedding and drug treatment of hyperlipidemia compared, which is better. Condition being studied: Hyperlipidemia. Information sources: Two authors (JL and CZ) will examine the publications independently and extract data according to predefined criteria. RCTs will be assessed for the methodology, study design, inclusion and exclusion criteria, and outcome measures. The methodological quality of each RCT will be recorded for method of randomization, blinding, protocol violation, and allocation concealment Any disagreement will be resolved by consensus discussions with the senior member of the review team (XP W). Data to collect includes intervention and control measures, measured outcomes and statistical significant difference with regards to chewing gum.
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Luo, Jing, Chen Zhang, Mengjie Xia, and Yuelian Chen. Acupoint catgut embedding reduces postoperative pain of mixed hemorrhoids: a meta-analysis of randomized controlled trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0021.

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Review question / Objective: Can acupoint catgut embedding alleviate postoperative pain of mixed hemorrhoids? Condition being studied: Mixed hemorrhoids. Information sources: Two authors (JL and CZ) will examine the publications independently and extract data according to predefined criteria. RCTs will be assessed for the methodology, study design, inclusion and exclusion criteria, and outcome measures. The methodological quality of each RCT will be recorded for method of randomization, blinding, protocol violation, and allocation concealment Any disagreement will be resolved by consensus discussions with the senior member of the review team (MJX and YLC). Data to collect includes intervention and control measures, measured outcomes and statistical significant difference with regards to chewing gum.
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Bourdeau, J. E., and R. D. Dyer. Regional-scale lake-sediment sampling and analytical protocols with examples from the Geological Survey of Canada. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331911.

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Regional-scale lake sediment surveys have been successfully used since the 1970s as a means for reconnaissance geochemical exploration. Lake sediment sampling is typically performed in areas with a lack of streams and an overabundance of small-sized (=5 km across) lakes. Lake sediments are known to have major, minor and trace element concentrations that reflect the local geology. Overall, lake sediment surveys are planned and conducted following four distinct stages: 1) background research, 2) orientation survey, 3) regional survey, and 4) detailed survey. At the Geological Survey of Canada, samples are usually collected from a helicopter with floats. Sample density ranges from 1 sample per 6 - 13 km2. Samples are collected from the centre of the lake using a gravity torpedo sampler which corresponds to a hollow-pipe, butterfly bottom-valved sampler attached by a rope to the helicopter. Collected sediment samples are then placed in labelled bags and left to air dry. Detailed field notes and additional samples (field duplicates), for the purpose of an adequate quality assurance and quality control program, are also taken. Samples are then milled and sent to analytical laboratories for element determination. Commonly used analytical methods include: X-ray fluorescence (XRF), atomic absorption spectroscopy (AAS), inductively coupled plasma-atomic emission spectrometry (ICP-AES) and -mass spectrometry (ICP-MS), instrumental neutron activation analysis (INAA), and/or determination of volatile compounds and organic carbon using Loss on Ignition (LOI). Analytical data is first evaluated for quality (contamination, accuracy and precision). Numerous options for the analysis of lake sediment data exist, ranging from simple basic element concentration maps and statistical graphical displays together with summary statistics, to employing multivariate methodologies, and, more recently, using machine learning algorithms. By adopting the set of guidelines and examples presented in this manual, scientific researchers, exploration geologists, geochemists and citizen scientists will be able to directly compare lake sediment datasets from anywhere in Canada.
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Haddock, John E., Reyhaneh Rahbar-Rastegar, M. Reza Pouranian, Miguel Montoya, and Harsh Patel. Implementing the Superpave 5 Asphalt Mixture Design Method in Indiana. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317127.

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Recent research developments have indicated that asphalt mixture durability and pavement life can be increased by modifying the Superpave asphalt mixture design method to achieve an in-place density of 95%, approximately 2% higher than the density requirements of conventionally designed Superpave mixtures. Doing so requires increasing the design air voids content to 5% and making changes to the mixture aggregate gradation so that effective binder content is not lowered. After successful laboratory testing of this modified mixture design method, known as Superpave 5, two controlled field trials and one full scale demonstration project, the Indiana Department of Transportation (INDOT) let 12 trial projects across the six INDOT districts based on the design method. The Purdue University research team was tasked with observing the implementation of the Superpave 5 mixture design method, documenting the construction and completing an in-depth analysis of the quality control and quality assurance (QC/QA) data obtained from the projects. QC and QA data for each construction project were examined using various statistical metrics to determine construction performance with respect to INDOT Superpave 5 specifications. The data indicate that, on average, the contractors achieved 5% laboratory air voids, which coincides with the Superpave 5 recommendation of 5%. However, on average, the as-constructed mat density of 93.8% is roughly 1% less than the INDOT Superpave 5 specification. It is recommended that INDOT monitor performance of the Superpave 5 mixtures and implement some type of additional training for contractor personnel, in order to help them increase their understanding of Superpave 5 concepts and how best to implement the design method in their operation.
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Krajcsik, Stephen. The Use of Statistical Methods in Dimensional Process Control. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada444590.

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Tannenbaum, Allen. Statistical and Variational Methods for Problems in Visual Control. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada531631.

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Shanahan, K. L. A suite of RS/1 procedures for chemical laboratory statistical quality control and Shewhart control charting. Office of Scientific and Technical Information (OSTI), September 1990. http://dx.doi.org/10.2172/6267900.

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Abbott, Pamela, Dickson Malunda, and Ismael Byaruhanga. Assessing the Impact and Scalability of Participatory Homegrown Programs on Reducing and Redistributing Unpaid Care Work among Women in Rwanda: A Case of Reseaux des Femmes' Unpaid Care Work Project in Rwanda: Baseline Report. Centre for Global Development, University of Aberdeen, 2023. http://dx.doi.org/10.57064/2164/21125.

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This report provides a descriptive overview of the quantitative baseline data collected in January and February 2022 for a research project evaluating a complex social intervention to reduce and redistribute women’s unpaid care work (UCW) in Rwanda using homegrown solutions. The intervention aims to reduce and redistribute UCW undertaken by women in Rwanda's rural areas, thereby improving their quality of life and increasing their empowerment. The findings discussed in this report are from a survey of intervention and control households and 7-day time diaries completed by husbands and wives in each household, with some illustrative material from simultaneous qualitative research. The research design for the project is a cluster trial informed by critical realism (CRCT) , combining quantitative and qualitative research methods to explain what works for whom under what circumstances. The intention is not just to identify the changes that can be attributed to the intervention but to develop explanatory theories of why the changes took place (Danermark et al., 2019; Porter et al., 2017; Porter and O’Halloran, 2012). The purpose of a Working Paper at this stage of the project is mostly to describe the lives and subordination of rural women as revealed by the baseline survey and, in the process, to identify any differences between control and intervention groups which have occurred by chance and will need to be controlled statistically in the analysis of the final results.
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Phero, Timothy, Amey Khanolkar, Kiyo Fujimoto, James Smith, and Michael McMurtrey. Development of Quality Control Methods for Robust and Reliable Sensor Design. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1901810.

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Helmreich, Grant, Katherine Montoya, Will Cureton, Eddie Lopez Honorato, Tyler Gerczak, and John Hunn. Quality Control Methods for Measurement of UCO Kernel Composition and SiC Microstructure. Office of Scientific and Technical Information (OSTI), December 2023. http://dx.doi.org/10.2172/2311297.

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