Journal articles: '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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Aziz, Muchammad Rif'an, Moh Jufriyanto, and Hidayat Hidayat. "Analysis Quality Control In Rice Packing Projects To Reduce Defects Using Methods Statistical Quality Control (SQC)." Jurnal Sains dan Teknologi Industri 20, no. 2 (March 23, 2023): 800. http://dx.doi.org/10.24014/sitekin.v20i2.22147.

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The industry is growing rapidly, indirectly forcing business actors to pay attention to the quality of the goods they produce during the production process. Companies must maintain the quality of their products because quality affects how satisfied customers are with a product. PT. XYZ is a company engaged in steel construction and fabrication. The problems that often occur are defects such as Overlap welding defects, Porosity welding defects, and unavoidable Cutting process defects. This study aims to determine the level of damage, identify the factors that cause damage and recommend corrective actions. Statistical quality control methods are used to identify the causes of defects in the production process, with statistical tools including: check sheets, histograms, Pareto charts, Control Chart diagrams, and fishbone diagrams. The results showed that product damage or defects were within reasonable limits but corrective action was needed to reduce defects. It can be seen from the Pareto diagram that the Overlap welding defects are 37.5% more than all product defects. Proposed improvements that need to be implemented by the company are cleaning the material before welding, performing treatment on the welding wire according to the Quality Control department's recommendations, checking the machine's stability before use and carrying out machine maintenance once a week.

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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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Eskov, D., D. Tsibaev, and V. Eskova. "Statistical methods for quality control sowing seeds on the forest clearing." Актуальные направления научных исследований XXI века: теория и практика 3, no. 2 (May 1, 2015): 38–42. http://dx.doi.org/10.12737/11025.

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Kazantseva, L. L., S. E. Artemenko, T. P. Ustinova, and V. N. Marinchev. "Use of statistical control methods for assessing the quality of polycaproamide." Fibre Chemistry 32, no. 6 (November 2000): 461–67. http://dx.doi.org/10.1007/bf02359083.

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Grigoriev, S. N., P. N. Emelianov, D. A. Masterenko, and S. E. Ped’. "Estimates of Rayleigh distribution parameters in statistical methods of quality control." Journal of Physics: Conference Series 2573, no. 1 (September 1, 2023): 012050. http://dx.doi.org/10.1088/1742-6596/2573/1/012050.

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Abstract The paper considers the problem of estimating of a two-parameter generalization of Rayleigh distribution and finding distributions of estimates. This problem arose from the needs of statistical sampling inspection of dimensions obtained in the production process with certain features of the part basing and cylindrical surface processing. Parameter estimates are found using order statistics. Computational procedures are constructed in the Matlab environment, which make it possible to construct distributions of estimates in the form of tables. The correctness of the calculations was verified using stochastic simulation and the construction of empirical distribution functions of estimates. The results obtained can be used to build operational characteristics of control plans and select control plans with the required producer’s and customer’s risks, as well as to build control charts for statistical control of the manufacturing process.

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Golińska, Ewa, and Marcin Zemczak. "Statistical Methods in the Automotive Industry." Applied Mechanics and Materials 791 (September 2015): 56–62. http://dx.doi.org/10.4028/www.scientific.net/amm.791.56.

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Controlling of the manufacturing processes is one of the key factors that allow companies to produce goods of high quality. In the article one of methods of the quality management - statistical process control (SPC) has been discussed. On the example of the car headrests manufacturing enterprise benefits from applying this method have been presented. Authors pointed out that the use of statistical methods in decision-making processes must not be difficult and time-consuming, if is being supported by a computer with using inexpensive tools.

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Hussain Jawad, Ethar. "Statistical Methods for Controlling the Quality of Crude Oil Products in Iraq." Journal of Economics and Administrative Sciences 29, no. 136 (June 5, 2023): 101–20. http://dx.doi.org/10.33095/jeas.v29i136.2611.

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The purpose of this study is to measure the levels of quality control for some crude oil products in Iraqi refineries, and how they are close to the international standards, through the application of statistical methods in quality control of oil products in Iraqi refineries. Where the answers of the study sample were applied to a group of Iraqi refinery employees (Al-Dora refinery, Al-Nasiriyah refinery, and Al-Basra refinery) on the principles of quality management control, and according to the different personal characteristics (gender, age, academic qualification, number of years of experience, job level). In order to achieve the objectives of the study, a questionnaire that included (12) items, in order to collect preliminary information about the study sample was designed. The data was analyzed statistically and hypotheses were tested using the SPSS statistical package. The study relied on a random sample of 225 individuals, of which 205 were valid. The most important results obtained are: The degree of applying control over quality management in the refineries under study was: 68.8% for the principle of senior management's commitment to quality, 65.2% for the principle of planning for the application of quality standards, and 65.% for the principle of strengthening the culture of quality among workers. The level of quality of some crude oil products in the refineries under study was estimated at 45%. In addition to the existence of a statistically significant correlation between the application of the principles of control over quality management and the quality of some oil products.

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Fathiya, Nurul, Wawan Kurniawan, Mustamina Maulani, and Wegig Murwonugroho. "Analysis of Surgical Suture Production Process Control Using Statistical Process Control (SPC) Methods." E3S Web of Conferences 500 (2024): 03031. http://dx.doi.org/10.1051/e3sconf/202450003031.

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The medical industry currently has very high-quality standards for medical products such as surgical sutures. PT XYZ, as a well-known manufacturer in this industry, faces challenges in reducing the level of surgical suture product defects. This research focuses on the surgical suture production process produced by PT XYZ. One strategy to gain a competitive advantage is to continuously improve the quality of its products. This condition must be supported by the implementation of quality control in the process so that it can run well to produce products that have high competitiveness. The aim of this research is to analyze whether the surgical suture production process is statistically controlled or not and to analyze whether the production process meets the specified design or not. The data used in this research are secondary data and primary data. Primary data was obtained through direct observation and interviews, while secondary data was obtained from the internet, literature and journals. This research uses statictical process control (SPC) as an analysis tool by creating X and R control charts and analyzing process capabilities. The research results show that the control of the surgical suture production process is a reliable process. This is an indication that the process is under control or is not experiencing deviations. The process capability ratio shows that the process is said to be feasible and does not need to be improved. The process capability index shows that the process accuracy is good, which means that the process does not need to be improved.

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Eygü, Hakan, and M. Suphi Özçomak. "Multivariate Statistical Quality Control Based on Ranked Set Sampling." Asian Social Science 14, no. 1 (December 14, 2017): 1. http://dx.doi.org/10.5539/ass.v14n1p1.

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The sample of the study was formed using simple random sampling, ranked set sampling, extreme ranked set sampling and median ranked set sampling. At the end of this process, the researcher created Hotelling’s T2 control charts, a multivariate statistical process control method. The performances of SRS, RSS, ERSS and MRSS sampling methods were compared to one another using these control charts. A simulation was performed to see the average run-length values for Hotelling’s T2 control charts, and these findings were also used for the comparison of the sampling performances.At the end of the study, the researcher formed a sample using median ranked set sampling and created the Hotelling’s T2 control chart. As a result of this operation, the researcher found that there was an out-of-control signal in the process, while there was no such signal in other sampling methods. When the average run-length values obtained from Hotelling’s T2 control charts were compared, it was seen that a shift in the process was detected by the ranked set sampling earlier, when compared to other sampling methods. This paper it can be said that the methods used are unique to the literature because they are applied to multivariate data.

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Benneyan, James C. "Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues." Infection Control and Hospital Epidemiology 19, no. 4 (April 1998): 265–83. http://dx.doi.org/10.2307/30142419.

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Benneyan, James C. "Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues." Infection Control and Hospital Epidemiology 19, no. 4 (April 1998): 265–83. http://dx.doi.org/10.1086/647807.

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Birnbaum, David, and James C. Benneyan. "Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues." Infection Control & Hospital Epidemiology 19, no. 4 (April 1998): 265–83. http://dx.doi.org/10.1017/s0195941700087397.

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ABSTRACTThis is the second in a two-part series discussing and illustrating the application of statistical process control (SPC) in hospital epidemiology. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand the mutual understanding and cross-fertilization between these two disciplines. Part I provided an overview of the philosophy and general approach of SPC, illustrated common types of control charts, and provided references for further information or statistical formulae. Part II now discusses alternate possible SPC approaches, statistical properties of control charts, chart-design issues and optimal control limit widths, some common misunderstandings, and more advanced issues. The focus of both articles is mostly nonmathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.

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Sumiati, Nur Rahmawati, Tranggono, Dwi Sukma Donoriyanto, and Minto Waluyo. "Study of Statistical Quality Control (SQC) Methods in Analyzing Quality Control of Paving Block Products at PT. X." Technium: Romanian Journal of Applied Sciences and Technology 16 (October 29, 2023): 436–42. http://dx.doi.org/10.47577/technium.v16i.10025.

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PT. X is a manufacturing company that produces building materials. PT. X is still often faced with several problems including the frequent occurrence of defective products, especially in paving block products as one of its main products. The defects that often occur include products that are easily crushed, cracked, asymmetrical, not dry and also broken in some parts. The defects that occur affect the quality of the resulting product. The purpose of this study was to determine the most dominant percentage of defects to allow further treatment. The method used in this research is Statistical Quality Control (SQC). The SQC method uses several tools including check sheets, stratification, histograms, paretto diagrams, scatter diagrams, control charts, and cause and effect diagrams. Based on the results of the SQC study, the most dominant defects were easily crushed paving with a percentage of 38.34%, cracked paving with a percentage of 28.86%, broken paving in some parts with a percentage of 13.83%, asymmetrical paving with a percentage of 10.92%, and non-dry paving with a percentage of 8.05%.

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Ilstrup, D. M. "Statistical methods in microbiology." Clinical Microbiology Reviews 3, no. 3 (July 1990): 219–26. http://dx.doi.org/10.1128/cmr.3.3.219.

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Statistical methodology is viewed by the average laboratory scientist, or physician, sometimes with fear and trepidation, occasionally with loathing, and seldom with fondness. Statistics may never be loved by the medical community, but it does not have to be hated by them. It is true that statistical science is sometimes highly mathematical, always philosophical, and occasionally obtuse, but for the majority of medical studies it can be made palatable. The goal of this article has been to outline a finite set of methods of analysis that investigators should choose based on the nature of the variable being studied and the design of the experiment. The reader is encouraged to seek the advice of a professional statistician when there is any doubt about the appropriate method of analysis. A statistician can also help the investigator with problems that have nothing to do with statistical tests, such as quality control, choice of response variable and comparison groups, randomization, and blinding of assessment of response variables.

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Khasanov, I. "SPC METHOD OF STATISTICAL CONTROL OF PROCESSES." European Journal of Contemporary Business Law & Technology: Cyber Law, Blockchain, and Legal Innovations 1, no. 3 (April 19, 2024): 80–86. http://dx.doi.org/10.61796/ejcblt.v1i3.433.

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In recent decades, the concept of quality has become more and more important. As for the production process, the implementation of quality control allows manufacturing enterprises to develop a high-quality product, which positively affects consumer satisfaction. This article aims to reflect the importance of the method of statistical control of quality processes. In this article, the SPC (Statistical Process Control) method of statistical control of processes, which is one of the effective methods used to prevent inconsistency in technological processes, is explained.

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Medvedev, Rostislav, and Yuliya Bakhracheva. "Application of Statistical Quality Control Methods for Optimizing the Track Repair Production." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 10. Innovatcionnaia deiatel’nost’, no. 3 (October 20, 2014): 93–97. http://dx.doi.org/10.15688/jvolsu10.2014.3.11.

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Temasova, G. N., O. A. Leonov, N. Zh Shkaruba, Yu G. Vergazova, and D. I. Petrovskiy. "Application of statistical methods for quality control of agro-industrial production processes." IOP Conference Series: Earth and Environmental Science 839, no. 2 (September 1, 2021): 022026. http://dx.doi.org/10.1088/1755-1315/839/2/022026.

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Abstract The article deals with the mechanism of quality control of agro-industrial production processes, namely, quality control of poultry farm processes. It also details the management of nonconforming products using simple quality control and management tools – a checklist and a Pareto chart. It was suggested that a checklist should be used to record nonconformities and their number for each nonconformity. Using this checklist form will allow you to analyze the production process, identify the causes of nonconformities, and quickly intervene in the process to prevent nonconformities from occurring. To analyze the inconsistencies and the causes of their occurrence, it was suggested to use the Pareto diagram, the use of which allows you to pay attention to the most significant problems.

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Kbiladze, David, and Shorena Metreveli. "THE SEARCH FOR MODERN METHODS FOR THE STATISTICAL QUALITY CONTROL OF SERVICES." Ekonomika 96, no. 2 (November 2, 2017): 102–9. http://dx.doi.org/10.15388/ekon.2017.2.11003.

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The countries with developed market economies have vast experience in using the statistical methods in running business. In this respect, the use of Six Sigma techniques and tools to control the output (service) quality is worthwhile. This technique, if introduced in Georgia, can be much beneficial for the businessmen. Different-profile companies use different statistical methods for the output (service) quality control. However, it is the Six Sigma techniques that yield the most pragmatic outputs. This tool enables the different-tier managers to rely not only on their intuition in making managerial decisions but also on the quantitative data obtained through the introduction of 6σ to their companies. The measures to search for the modern methods of introduction and better use of the 6σ concept seek to improve the output (production) quality of different sites of a corporation, while also meeting the changing demands of customer markets and shareholders. In the search of such measures, the authors of the present article attempted to examine the effect of the 6σ set beyond the limits of a corporation and, by considering the public opinion about the corporation, conduct the statistical control of the output (service) offered by the corporation to its clients. The pilot study, accomplished with this purpose by the authors of the article, together with their students, gave quite interesting results.

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Makarov, R. I., V. I. Mazanova, and Yu M. Obukhov. "The process approach and statistical methods in quality control of hardened glass." Glass and Ceramics 65, no. 9-10 (September 2008): 328–30. http://dx.doi.org/10.1007/s10717-009-9069-y.

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Lu, Jye-Chyi, Shuen-Lin Jeng, and Kaibo Wang. "A Review of Statistical Methods for Quality Improvement and Control in Nanotechnology." Journal of Quality Technology 41, no. 2 (April 2009): 148–64. http://dx.doi.org/10.1080/00224065.2009.11917770.

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Montgomery, Douglas C., and Christina M. Mastrangelo. "Some Statistical Process Control Methods for Autocorrelated Data." Journal of Quality Technology 23, no. 3 (July 1991): 179–93. http://dx.doi.org/10.1080/00224065.1991.11979321.

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Zhuravlev, G. M., A. E. Gvozdev, S. V. Sapozhnikov, S. N. Kutepov, and E. V. Ageev. "DECISIONS ON STATISTICAL MODELS IN QUALITY CONTROL OF PRODUCTS." Proceedings of the Southwest State University 21, no. 5 (October 28, 2017): 78–92. http://dx.doi.org/10.21869/2223-1560-2017-21-5-78-92.

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Development of methods for registration, description and analysis of statistical experimental data, obtained by monitoring mass random phenomena is the subject of a special science - mathematical statistics. All tasks of mathematical statistics concerns the treatment of observations of mass random phenomena, but depending on the nature of the solved practical question and amount of available experimental material these tasks can take a particular form. One of the main objectives of mathematical statistics is to develop methods of studying mass phenomena or processes on the basis of the relatively small number of observations or experiments. These methods have their scientific justification, his theory, called the theory of samples. The aim of this work is to build mathematical models of influence of various factors on a single number using the method of multifactor experiment planning, and their use results in the appointment of modes of technological operations. To study processes incomplete hot deformation uses a complex viscoplastic model of the environment, the mechanical properties which are characterized by a yield stress and viscosity. The yield strength depends on temperature and strain rate. On this basis, was carried out processing of experimental data by the method of multifactor experiment planning and statistical treatment of experimental data by definition of the yield strength depending on temperature and speed of deformation of steel U12A. From the analysis of the obtained regression equations, we can conclude that the most highly specific force depends on temperature. Regression equations mathematically describe the mutual influence of technological factors on yield strength and specific strength, in addition they allow you to correctly set processing modes that yield products of the required quality.

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Wang, Wenbin, and Wenjuan Zhang. "Early defect identification: application of statistical process control methods." Journal of Quality in Maintenance Engineering 14, no. 3 (August 15, 2008): 225–36. http://dx.doi.org/10.1108/13552510810899445.

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Salma, Jihan, and Resi Juariah Susanto. "The Quality Control Analysis Using Statistical Quality Control (SQC) Methods in Reducing Defective Muslim Clothing Products on CV. Mawaheejra." Banking and Management Review 12, no. 1 (January 23, 2024): 18–28. http://dx.doi.org/10.52250/bmr.v12i1.732.

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Introduction/Main Objectives: This study determines the quality control of CV. Mawaheejra, determine the factors causing product defects and the application of Statistical Quality Control (SQC) methods. Background Problems: CV. Mawaheejra has carried out control but the quality control is not optimal because of poor quality and errors often occur in the production process which causes product defects that not comply with the quality standards. Research Methods: This study uses a quantitative method. It carried data collection techniques through observation, interviews, documentation, and literature studies. Data analysis using Statistical Quality Control (SQC) methods of flowcharts, check sheets, Pareto charts, control charts, and fishbone diagrams. Finding/Results: Based on the results of research with check sheet aids found four types of defects. In the Pareto chart found the highest number of product disability percentage of 38.47%. From the control chart result, it found deviations because of the U control chart there are three points that exceed UCL and two points that exceed LCL. From the fishbone diagram, there are five factors that are most prominently caused by human and machine factors. Conclusion: Based on the results of the analysis, the company must conduct supervision of employees and machine maintenance to reduce product defects.

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Thomann, M. "Quality evaluation methods for wastewater treatment plant data." Water Science and Technology 57, no. 10 (May 1, 2008): 1601–9. http://dx.doi.org/10.2166/wst.2008.151.

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Non identified systematic errors in data sets can cause severe problems inducing wrong decisions in function control, process modelling or planning of new treatment infrastructure. In this paper statistical methods are shown to identify systematic errors in full-scale WWTP data sets. With a redundant mass balance approach analyzing five different mass balances, systematic errors of about 10%–20% compared to the input fluxes can be identified at a 5%-significance level. A Shewhart control-chart approach to survey the data quality of on-line-sensors allows a statistical as well as a fast graphical analysis of the measurement process. A 19 month data set indicates that NO3−, PO4− and NH4− on-line analyzers in the filter effluent and MLSS sensors in the aeration tanks were not disturbed by any systematic error for 85–95% of the measuring time. The in-control-interval (±3·standard deviation) has a width of ±12–17% (NO3-N), ±35–40% (PO4-P), ±83% (NH4-N) and ±12–15% (TS) of the measured reference value.

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Kleymenova, N. L., L. I. Nazina, I. N. Bolgova, A. N. Pegina, and O. A. Orlovseva. "Quality control in the production process of sunflower oil." IOP Conference Series: Earth and Environmental Science 845, no. 1 (November 1, 2021): 012111. http://dx.doi.org/10.1088/1755-1315/845/1/012111.

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Abstract The typical problem of vegetable oil processing is to ensure the consistency of the output quality. The one parameter that mostly affects quality is the presence of wax, which commands control at all stages of the process (refinement, odours removal, freezing). Statistical methods of analysis can be usefully applied to the improvement of vegetable oil processing, as demonstrated by this study. The authors in fact used statistical methods in order to: a) optimize parameters consistency, b) enhance process efficiency, c) improve economic performance and finally, d) assess process stability. The following statistical tools were used in the study: 1) Histograms, 2) Shewhart Charts, 3) Ishikawa Diagrams and, 4) Pareto Chart. A first major finding was that the occurrence of process flaws that would result in product rejection had a 5% probability of happening at all stages of the process. Moreover, the analysis of process stability with maps of average values and ranges leads to the finding that the process itself is statistically unstable. Finally, cause-and-effect relationships of influencing factors (such as the quality of feedstock) were investigated, thus determining the main causes of flaw in the production process. This leads to the definition of corrective actions, the effectiveness of which was then investigated and evaluated.

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Garrison, Eric, and Joshua New. "Quality Control Methods for Advanced Metering Infrastructure Data." Smart Cities 4, no. 1 (January 28, 2021): 195–203. http://dx.doi.org/10.3390/smartcities4010012.

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While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.

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Prístavka, Miroslav, Martina Kotorová, and Radovan Savov. "Quality Control in Production Processes." Acta Technologica Agriculturae 19, no. 3 (September 1, 2016): 77–83. http://dx.doi.org/10.1515/ata-2016-0016.

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AbstractThe tools for quality management are used for quality improvement throughout the whole Europe and developed countries. Simple statistics are considered one of the most basic methods. The goal was to apply the simple statistical methods to practice and to solve problems by using them. Selected methods are used for processing the list of internal discrepancies within the organization, and for identification of the root cause of the problem and its appropriate solution. Seven basic quality tools are simple graphical tools, but very effective in solving problems related to quality. They are called essential because they are suitable for people with at least basic knowledge in statistics; therefore, they can be used to solve the vast majority of problems.

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Kubalcik, Marek, Vladimir Bobal, and Tomas Barot. "Statistical analysis of control quality of MPC using testing hypothesis." MATEC Web of Conferences 292 (2019): 01037. http://dx.doi.org/10.1051/matecconf/201929201037.

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Methods of the statistical induction have a significant role in the quantitative research. In a wide spectrum of research areas, the methods based on testing hypotheses have been frequently used. However, in the area of the process control, testing hypothesis has not been widely considered as an established tool for signal analyses, although signals in control loops are suitable for analysis by means of quantitative statistical methods due to their stochastic character. Particularly, a statistical paired comparison can be applied for analysis of control quality achieved with different control algorithms. This comparison can be based on a paired comparison of corresponding signals obtained with different or modified control algorithms. The aim of this paper is a proposal of incorporation of testing hypothesis to analysis of control quality. The analysis was performed on a strictly defined significance level 0.001, which is a standardly used value in technical applications. As an example was demonstrated analysis of control quality achieved with two versions of a predictive controller. Finally, achieved results of paired comparison using testing hypothesis are discussed.

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Alba-Fernández, María V., Francisco J. Ariza-López, José Rodríguez-Avi, and José L. García-Balboa. "Statistical Methods for Thematic-Accuracy Quality Control Based on an Accurate Reference Sample." Remote Sensing 12, no. 5 (March 3, 2020): 816. http://dx.doi.org/10.3390/rs12050816.

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The goal of this work is to present a set of statistical tests that offer a formal procedure to make a decision as to whether a set of thematic quality specifications of a product is fulfilled within the philosophy of a quality control process. The tests can be applied to classification data in thematic quality control, in order to check if they are compliant with a set of specifications for correctly classified elements (e.g., at least 90% classification correctness for category A) and maximum levels of poor quality for confused elements (e.g., at most 5% of confusion is allowed between categories A and B). To achieve this objective, an accurate reference is needed. This premise entails changes in the distributional hypothesis over the classification data from a statistical point of view. Four statistical tests based on the binomial, chi-square, and multinomial distributions are stated, to provide a range of tests for controlling the quality of product per class, both categorically and globally. The proposal is illustrated with a complete example. Finally, a guide is provided to clarify the use of each test, as well as their pros and cons.

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Novianti, Fenny, and Rr Rochmoeljati. "Quality Control of Edamame Products Using Statistical Quality Control (SQC) and Failure Mode Effect Analysis (FMEA) Methods in PT. XYZ." IJIEM - Indonesian Journal of Industrial Engineering and Management 4, no. 2 (June 30, 2023): 221. http://dx.doi.org/10.22441/ijiem.v4i2.20550.

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PT. XYZ is a company engaged in the frozen food processing industry. One such product is Edamame. In the Edamame production process, defects such as pest defects, color defects, pod defects and mechanical defects that affect quality are still found. The purpose of this study was to determine the proportion of defects that most often occur and the factors that cause defects and to provide suggestions for improving the quality of edamame. The methods used are Statistical Quality Control (SQC) and Failure Mode Effect Analysis (FMEA). The SQC tools include check sheets, statistics, histograms, pareto charts, scatter diagrams, control charts, and fishbone diagrams. Then continue the FMEA analysis for suggestions for corrective action. Based on the results of research on Statistical Quality Control (SQC) it is known that the most dominant defects in edamame are pests (39.9%), then color (25.5%), pods (25.2%), and mechanics (9.4%) ). Based on the results of research on Failure Mode Effect Analysis (FMEA) it is known that the highest cause of problems with RPN 360 is lack of sanitation. Suggestions for improvement that can be proposed namely. Scheduling cleaning of the work environment area and sterilizing production machines and equipment.

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Jiang, Zhong Qiu. "The Intelligent Quality Control Technology System Based on the Integration Methods of SPC and EPC." Applied Mechanics and Materials 263-266 (December 2012): 839–42. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.839.

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SPC (statistical process control) and EPC (engineering process control) is the scientific methods of quality quality control and quality improvement. It is the difficult problems of quality control process for network manufacturing enterprise how to effectively solve the dynamic quality fluctuation monitoring and the fluctuation abnormal diagnostic analysis and timely process adjustments, this paper designs the intelligent quality control mode and function system architecture, the modes expatiates quality management network based on quality control network of the workshop level and enterprise class network, and researches the integration applications of statistical process control and enterprise ERP quality system, and applies J2EE technology to achieve the system organic combination of design and development.

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Gunter, Bert, Christine Brideau, Bill Pikounis, and Andy Liaw. "Statistical and Graphical Methods for Quality Control Determination of High-Throughput Screening Data." Journal of Biomolecular Screening 8, no. 6 (December 2003): 624–33. http://dx.doi.org/10.1177/1087057103258284.

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High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good quality data and mini mize assay variability while maintaining assay sensitivity. The authors describe new QC methods and show numerous real examples from their biologist-friendly Stat Server® HTS application, a custom-developed software tool built from the commercially available S-PLUS® and Stat Server® statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables. It allows users to visualize HTS data and examine assay performance during the HTS campaign to quickly react to or avoid quality problems.

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Childress, Ashley. "Statistical Methods for Quality Assurance: Basics, Measurement, Control, Capability, and Improvement, Second Edition." Journal of Quality Technology 51, no. 2 (February 8, 2018): 214–15. http://dx.doi.org/10.1080/00224065.2018.1404872.

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Jin, Yuan, Mark Carman, Ye Zhu, and Yong Xiang. "A technical survey on statistical modelling and design methods for crowdsourcing quality control." Artificial Intelligence 287 (October 2020): 103351. http://dx.doi.org/10.1016/j.artint.2020.103351.

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Rossadi, Paradika, and Sumiati Sumiati. "Quality Control of Midsole Products Using Statistical Quality Control (SQC) and Failure Mode Effect Analysis (FMEA) Methods in PT. XYZ." IJIEM - Indonesian Journal of Industrial Engineering and Management 4, no. 3 (December 27, 2023): 495. http://dx.doi.org/10.22441/ijiem.v4i3.22406.

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PT. XYZ is a company engaged in the shoes industry. One such product is Midsole. In the Midsole production process, defects such as burning defects, tearing defects, bubble defects and dirty defects that affect quality are still found. The purpose of this study was to determine the proportion of defects that most often occur and the factors that cause defects and to provide suggestions for improving the quality of Midsole. The methods used are Statistical Quality Control (SQC) and Failure Mode Effect Analysis (FMEA). The SQC tools include check sheets, statistics, histograms, pareto charts, scatter diagrams, control charts, and fishbone diagrams. Then continue the FMEA analysis for suggestions for corrective action. Based on the results of research on Statistical Quality Control (SQC) it is known that the most dominant defects in midsole are burning (38%), then dirty (24,4%), bubble (20%), and tearing (17,6%) ). Based on the results of research on Failure Mode Effect Analysis (FMEA) it is known that the highest cause of problems with RPN 343 is Mold Release Agent spraying is uneven. Suggestions for improvement that can be proposed namely. Conduct training for workers on how to spray MRA according to stamdart and determine the rest time limit so that the injection engine does not overheat.

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FIGIEL, KERRY, and MICHAEL FORBES. "The evolution of reel statistical methods." June 2019 18, no. 6 (July 1, 2019): 365–77. http://dx.doi.org/10.32964/tj18.6.365.

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Multiple statistical methods for calculating the variance partition analysis (VPA) of reel data have existed for decades. In the paper industry, VPA is also commonly known as reel statistics. VPA commonly consists of total variance (TOT) that is then divided into three components: cross direction (CD), machine direction (MD), and residual (RES). A common mathematical procedure is referred to as ANOVA (analysis of variance). TAPPI Standard Test Method T 545 “Cross-machine grammage profile measurement (gravimetric method)” addresses paper testing and includes the ANOVA equations that have also been used to analyze scanning data. In the 1990s, TAPPI published TIP 1101-01 “Calculation and partitioning of variance using paper machine scanning sensor measurements,” which contained simple formulas that were easy to implement and could be used by a novice to generate statistics on a spreadsheet. All involved quality control system (QCS) suppliers agreed to support this common method in their QCS. TIP 1101 was recently revised, and this paper concerns the analysis of data collected from a scanning sensor in a QCS and the creation of a common method for the calculation of reel statistics by TAPPI’s Process Control Division.

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Boyacioglu, Hülya, and Hayal Boyacioglu. "Application of environmetric methods to investigate control factors on water quality." Archives of Environmental Protection 43, no. 3 (September 1, 2017): 17–23. http://dx.doi.org/10.1515/aep-2017-0026.

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AbstractIn the study, environmetric methods were successfully performed a) to explore natural and anthropogenic controls on reservoir water quality, b) to investigate spatial and temporal differences in quality, and c) to determine quality variables discriminating three reservoirs in Izmir, Turkey. Results showed that overall water quality was mainly governed by “natural factors” in the whole region. A parameter that was the most important in contributing to water quality variation for one reservoir was not important for another. Between summer and winter periods, difference in arsenic concentrations were statistically significant in the Tahtalı, Ürkmez and iron concentrations were in the Balçova reservoirs. Observation of high/low levels in two seasons was explained by different processes as for instance, dilution from runoff at times of high flow seeped through soil and entered the river along with the rainwater run-off and adsorption. Three variables “boron, arsenic and sulphate” discriminated quality among Balçova & Tahtalı, Balçova & Ürkmez and two variables “zinc and arsenic” among the Tahtalı & Ürkmez reservoirs. The results illustrated the usefulness of multivariate statistical techniques to fingerprint pollution sources and investigate temporal/spatial variations in water quality.

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Benziane, Sarah. "Survey: Rainfall Prediction Precipitation, Review of Statistical Methods." WSEAS TRANSACTIONS ON SYSTEMS 23 (January 8, 2024): 47–59. http://dx.doi.org/10.37394/23202.2024.23.5.

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Rainfall precipitation prediction is the process of using various models and data sources to predict the amount and timing of precipitation, such as rain or snow, in a particular location. This is an important process because it can help us prepare for severe weather events, such as floods, droughts, and hurricanes, as well as plan our daily activities. Processing rainfall data typically involves several steps, which may vary depending on the specific data set and research question. Here is a general overview of the steps involved: (1) Collecting data: Rainfall data can be collected using various methods, including rain gauges, radar, and satellite imagery. The data can be obtained from public sources, such as government agencies or research institutions. (2) Quality control: Before using the data, it's important to check for errors or inconsistencies. This may involve identifying missing or incomplete data, outliers, or inconsistencies in measurement units. Quality control can be performed manually or using automated software. (3) Pre-processing: Once the data has been quality controlled, it may need to be pre-processed for analysis. This may involve aggregating the data to a specific temporal or spatial resolution, such as daily, monthly, or annual averages, or converting the data to a specific format. (4) Analysis: The processed data can be used for various types of analysis, such as trend analysis, frequency analysis, or spatial analysis. These analyses can help to identify patterns, changes, or relationships in the data. (5) Visualization: Finally, the results of the analysis can be visualized using graphs, maps, or other types of visualizations to help communicate the findings. Overall, processing rainfall data requires careful attention to detail and a clear understanding of the research question and data sources.

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

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