Relevant bibliographies by topics / Statistical quality control 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 'Statistical quality control methods'

Author: Grafiati
Published: 28 May 2022

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Journal articles on the topic "Statistical quality control 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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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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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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Stepanov, A. M., and N. V. Khashimova. "USE OF STATISTICAL QUALITY CONTROL METHODS IN MANUFACTURING BRIDGE METAL STRUCTURES." Kontrol'. Diagnostika, no. 253 (July 2019): 30–37. http://dx.doi.org/10.14489/td.2019.07.pp.030-037.

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This article describes the use of statistical methods of quality control and management in the manufacturing metal bridge structures, such as: – cause and effect diagram (Ishikawa diagram). Causal factors affecting the quality of welded joints were established; – checklist. The form of the control sheet for registration of types of surface defects of butt welded joints were developed; – pareto Chart. The most common types of surface defects of butt welded joints were identified; the types of surface defects of butt welded joints, leading to the greatest losses were determined. ABC method – analysis of Pareto diagram of internal defects of welded joints. General conclusions on the results of their application were made.
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Simonenko, K. V., G. S. Bulatova, L. B. Antropova, and L. G. Varepo. "Quality Control of the Print with the Application of Statistical Methods." Journal of Physics: Conference Series 998 (April 2018): 012032. http://dx.doi.org/10.1088/1742-6596/998/1/012032.

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Dissertations / Theses on the topic "Statistical quality control 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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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.
QC 20100909
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Driesen, Kevin E. "Statistical process control as quantitative method to monitor and improve medical quality." Diss., The University of Arizona, 2004. http://hdl.handle.net/10150/280602.

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Statistical Process Control (SPC) methods, developed in industrial settings, are increasingly being generalized to medical service environments. Of special interest is the control chart, a graphic and statistical procedure used to monitor and control variation. This dissertation evaluates the validity of the control chart model to improve medical quality. The research design combines descriptive and causal comparative (ex-post facto) methods to address the principal research question, How is the control chart model related to medical quality? Hospital data were used for patients diagnosed with Community Acquired Pneumonia (CAP). During the initial research phase, five medical quality "events" assumed to affect CAP medical quality indicators were pre-specified by hospital staff. The impact of each event was then evaluated using control charts constructed for CAP quality indicators. Descriptive analysis was undertaken to determine whether data violated the statistical assumptions underlying the control chart model. Then, variable and attribute control charts were constructed to determine whether special cause signals occurred in association with the pre-specified events. Alternative methods were used to calibrate charts to different conditions. Sensitivity was computed as the proportion of event-sensitive signals. The descriptive analysis of CAP indicators uncovered "messy," and somewhat complex, data structure. The CAP indicators were marginally stable showing trend, seasonal cycles, skew, sampling variation and autocorrelation. Study results need to be interpreted with the knowledge that few events were evaluated, and that the effect sizes associated with events were small. The charts applied to the CAP indicators showed limited sensitivity; for three chart-types (i.e. XmR, Xbar, and P-charts), there were more false alarms than event-associated signals. Conforming to expectation, larger sample size increased chart sensitivity. The application of Jaehn Decision Rules led to increases in both sensitivity and false alarm. Increasing subgroup frequency from month, to week samples, increased chart sensitivity, but also increased data instability and autocorrelation. Contrary to expectation, the application of hybrid charting techniques (EWMA and CUSUM) did not increase chart sensitivity. Study findings support the conclusion that control charts provide valuable insight into medical variation. However, design issues, data character, and causal logic provide conditions to the interpretation of control charts.
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Minardi, Michael. "Comparing process capability : a c pk ratio approach." Honors in the Major Thesis, University of Central Florida, 2001. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/288.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.
Bachelors
Arts and Sciences
Statistics
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Books on the topic "Statistical quality control methods"

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Statistical quality control. Boca Raton, FL: CRC Press, 2001.

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Statistical methods for quality improvement. New York: Wiley, 1989.

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

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

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

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

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

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

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Statistical quality assurance. Albany, N.Y: Delmar, 1987.

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Statistical methods for quality improvement. 3rd ed. Hoboken, N.J: Wiley, 2011.

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Book chapters on the topic "Statistical quality control 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, 147–52. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4684-0041-0_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, 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, 151–55. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0149-7_6.

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Conference papers on the topic "Statistical quality control 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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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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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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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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Naeini, M. Kabiri, M. S. Owlia, and M. S. Fallahnezhad. "A new statistical method for recognition of control chart patterns." In 2011 IEEE International Conference on Quality and Reliability (ICQR). IEEE, 2011. http://dx.doi.org/10.1109/icqr.2011.6031612.

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García-Díaz, J. Carlos, and Vicente Jesus Segui. "Statistical Methods for Quality Control of Steel Coils Manufacturing Process using Generalized Linear Models." In THIRD MANUFACTURING ENGINEERING SOCIETY INTERNATIONAL CONFERENCE: MESIC-09. AIP, 2009. http://dx.doi.org/10.1063/1.3273644.

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Robinson, David C., Ebrahim Mazharsolook, and David A. Sanders. "The Practical Application of Advanced Statistical Techniques for Computer-Based Quality Control." In ASME 1993 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/cie1993-0047.

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Abstract Innovative methods are explored for computer based quality control of parts manufacture. The approach is based on the use of analysis of variance techniques and regression modelling to formulate a description (model) of the product and process in terms of product quality. This model is then employed in a computer based on-line quality control strategy in which the aim is to optimise manufacturing quality by attempting to compensate for product deviations. Available quality control software is based on methodologies developed in the 1920s [1]. It is concerned with the presentation of data and off-line analysis rather than the decision making process. By integrating statistics and quality control strategies, computer based decisions can be formulated based on larger quantities of data than a human could assimilate. Such decisions can be made more accurately and in a fraction of the time taken by manual means. A case study is presented which is concerned with the implementation of computer based on-line quality control of manufacture. This case study was successfully completed with a reduction in the first time reject rate from thirty percent to below four percent. The case study shows the generic nature of the method and also the diversity of application.
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Riadi, Doni, and Ade Geovania Azwar. "BASE SINGLE PART PRODUCT QUALITY CONTROL USING STATISTICAL QUALITY CONTROL (SQC) METHOD IN PT DIRGANTARA INDONESIA MACHINING DEPARTMENT." In Seminar Sosial Politik, Bisnis, Akuntansi dan Teknik (SoBAT) ke-3. LPPM USB YPKP, 2021. http://dx.doi.org/10.32897/sobat3.2021.45.

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Base Single Part is the main component of the cable installation that functions as a connector between cables in the aircraft. As a critical part, the Base Single Part is the most important sub-component that must be completed to be installed in the aircraft part. Base Single Part is a part that is used in all types of aircraft which are specially produced at PT. Dirgantara Indonesia, in one aircraft this component can be installed in the amount of 8000-9000 Base Single Part. In relation to the request from the Final Assembly Line section regarding the request for the Base Single Part component, there are still some products that do not meet the specifications so that they have to be repaired, such as holes that do not match the size, this causes the delivery order process to the Final Assembly Line section to be hampered and disruptive smooth production. The purpose of this research is to control the quality of the Base Single Part product by using a p control chart, and a causal diagram.The results of the study conclude that the calculation of the percentage level of defect in the Base Single Part component using the p control chart, there are 3 percentage points that are outside the control limit, namely in August with a disability percentage of 15.11%, in September 10.32%, and October 15.60%. Factors causing defects in Base Single Part components consist of 3 factors, human factors, method factors and machine factors. The human factor is workers who are less productive. Furthermore, the method factor is regarding the fluctuating production schedule and the last cause is the machine factor that lacks maintenance.
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Kazmer, David O., and Tatiana Petrova. "Synthesis and Analysis of Quality Control Methods for Intelligent Processing of Polymeric Materials." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0633.

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Abstract Global manufacturers of thermoplastic molded parts increasingly require 100% quality inspection levels that are difficult to achieve. While process complexity makes it difficult to attain the desired part properties during start-up. the stochastic nature of the process causes difficulty in maintaining part quality during production. This paper formally compares several alternative quality control methods that are currently utilized for processing of polymeric materials. To identify the technical issues associated with this goal, the injection molding process is described utilizing a control systems approach. Afterwards, four different methods of quality regulation are synthesized for injection molding: open loop quality control, statistical process control, trained parameter control, and on-line quality regression. For each strategy, the level of quality observability and controllability are determined against the dynamics of the manufacturing system. The results indicate that none of the quality regulation strategies have the underlying design architecture to deliver 100% quality assurance across a diverse set of application characteristics (quality requirements, material properties, mold geometries, and machine dynamics). As such, subsequent discussion focuses on defining the system requirements for achieving ‘intelligent’ processing of polymeric materials that are needed by industry.
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Reports on the topic "Statistical quality control methods"

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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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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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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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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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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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Al-Qadi, Imad, José Rivera-Perez, Hasan Ozer, Watheq Sayeh, Javier García Mainieri, Hadi Meidani, John Huang, and Adam Hand. Data Trends and Variability in Quality Control for Performance and Pay for Performance Specifications: Statistical Analysis. Illinois Center for Transportation, June 2020. http://dx.doi.org/10.36501/0197-9191/20-006.

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Olson, Edwin S. Task 2.0 - Air Quality Assessment, Control, and Analytical Methods Subtask 2.11 - Lactic Acid FGD Additives From Sugar Beet Wastewater. Office of Scientific and Technical Information (OSTI), February 1998. http://dx.doi.org/10.2172/1690.

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Lee, Jusang, John E. Haddock, Dario D. Batioja Alvarez, and Reyhaneh Rahbar Rastegar. Quality Control and Quality Assurance of Asphalt Mixtures Using Laboratory Rutting and Cracking Tests. Purdue University, 2019. http://dx.doi.org/10.5703/1288284317087.

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The main objectives of this project were to review the available balanced-mix design (BMD) methodologies, understand the I-FIT and Hamburg Wheel Tracking Test (HWTT) test methods using INDOT asphalt mixtures, and to explore the application of these tests to both a BMD approach and as performance-related Quality Control (QC) and Quality Acceptance (QA) methods. Two QA mixture specimen types, plant-mixed laboratory-compacted (PMLC) and plant-mixed field-compacted (PMFC) were used in the determination of cracking and rutting parameters. Distribution functions for the flexibility index (FI) values and rutting parameters were determined for various mixture types. The effects of specimen geometry and air voids contents on the calculated Flexibility Index (FI) and rutting parameters were investigated. The fatigue characteristics of selected asphalt mixtures were determined using the S-VECD test according to different FI levels for different conditions. A typical full-depth pavement section was implemented in FlexPAVE to explore the cracking characteristics of INDOT asphalt mixtures by investigating the relationship between the FI values of QA samples with the FlexPAVE pavement performance predictions. The FI values obtained from PMFC specimens were consistently higher than their corresponding PMLC specimens. This study also found that FI values were affected significantly by variations in specimen thickness and air voids contents, having higher FI values with higher air voids contents and thinner specimens. These observations do not agree with the general material-performance expectations that better cracking resistance is achieved with lower air voids content and thicker layers. Additionally, PG 70-22 mixtures show the lowest mean FI values followed by the PG 76-22 and 64-22 mixtures. The same order was observed from the ΔTc (asphalt binder cracking index) of INDOT’s 2017 and 2018 projects. Finally, it was found that the HWTT showed reasonable sensitivity to the different characteristics (e.g., aggregate sizes, binder types, and air voids contents) of asphalt mixtures. Mixtures containing modified asphalt binders showed better rut resistance and higher Rutting Resistance Index (RRI) than those containing unmodified binders.
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Meijer, G. J., H. J. Van Kleffens, and B. J. Mijnheer. NCS Report 11: Quality control (QC) of simulators and CT scanners and some basic QC methods for treatment planning systems. Delft: NCS, September 1997. http://dx.doi.org/10.25030/ncs-011.

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