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1

Kuhn, Andrew M. "Multivariate Statistical Methods in Quality Management." Technometrics 47, no. 1 (2005): 94–95. http://dx.doi.org/10.1198/tech.2005.s823.

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2

Beggs, William J. "Statistical Methods for Nuclear Material Management." Technometrics 32, no. 4 (1990): 462–63. http://dx.doi.org/10.1080/00401706.1990.10484752.

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3

Sahu, Anshuman. "Statistical methods in customer relationship management." Journal of Applied Statistics 41, no. 4 (2013): 912. http://dx.doi.org/10.1080/02664763.2013.853920.

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4

HRYTSUN, Viktoriia. "STATISTICAL SECURITY OF ECONOMIC MANAGEMENT." Herald of Khmelnytskyi National University. Economic sciences 318, no. 3 (2023): 180–85. http://dx.doi.org/10.31891/2307-5740-2023-318-3-28.

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Modern integrated economic statistics is a set of economic and statistical data that reflect a consistent and holistic picture of economic activity for political, commercial and other analytical purposes. In analyzing the historical aspect of the emergence and development of statistical practice, we have shown that its origin occurred around the time when the state emerged. There is information about the elementary recount (census) of population and land, which was conducted several millennia ago. With the formation of a centralized state and especially with its development, the scope of stati
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5

Farnum, Nicholas R. "Statistical Methods." Journal of Quality Technology 22, no. 3 (1990): 252–53. http://dx.doi.org/10.1080/00224065.1990.11979254.

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6

Wang, Yu, and Jian Zou. "Statistical methods for large portfolio risk management." Statistics and Its Interface 6, no. 4 (2013): 477–85. http://dx.doi.org/10.4310/sii.2013.v6.n4.a6.

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7

Kubečková, Darja, and Stanislav Smugala. "Statistical methods applied to construction process management." Asian Journal of Civil Engineering 21, no. 3 (2020): 479–94. http://dx.doi.org/10.1007/s42107-020-00221-7.

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8

Webster, R. "Using Statistical Methods for Water Quality Management." Journal of Environmental Quality 34, no. 6 (2005): 2338. http://dx.doi.org/10.2134/jeq2005.0017br.

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9

Kondratenko, N. O., T. М. Kolesnyk, and M. A. Bratash. "Statistical Methods in the Enterprise Development Management System." PROBLEMS OF ECONOMY 4, no. 50 (2021): 114–20. http://dx.doi.org/10.32983/2222-0712-2021-4-114-120.

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The article suggests a theoretical generalization of using statistical methods in the enterprise development management system. It is noted that the collection, arrangement and analysis of information on the current state and development perspectives of an economic entity is one of the most important enterprise development management instruments. That is, modern management methods require a reliable information supply, creation of a new strategy for collecting, processing and analyzing information. Statistical methods have been systematized according to their intended function. Statistical met
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10

Czarski, Andrzej, Kazimierz Satora, and Piotr Matusiewicz. "STATISTICAL METHODS IN QUALITY MANAGEMENT - PROCESS CAPABILITY ANALYSIS." Metallurgy and Foundry Engineering 33, no. 2 (2007): 121. http://dx.doi.org/10.7494/mafe.2007.33.2.121.

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11

Hand, DJ. "Statistical methods in diagnosis." Statistical Methods in Medical Research 1, no. 1 (1992): 49–67. http://dx.doi.org/10.1177/096228029200100104.

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12

Vojtková, Mária, and Patrik Mihalech. "Intraday liquidity modelling using statistical methods." Argumenta Oeconomica 2023, no. 2 (2023): 151–78. http://dx.doi.org/10.15611/aoe.2023.1.08.

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The correct approach to liquidity risk management in banks is essential for securing their financial stability. The position of liquidity risk among the rest of bank risks is specific because the negative outcome is not just a loss, but directly the bankruptcy of the institution. Such an occurrence might start a chain reaction and bring uncertainty into the entire financial system. This paper focused on one source of liquidity risk, i.e. management of liquidity throughout the day. The management of intraday liquidity is related to cash inflows and outflows occurring during the business day, th
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13

Prístavka, Miroslav, and Marián Bujna. "Use of Statistical Methods in Quality Control." Acta Technologica Agriculturae 16, no. 2 (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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14

Loyeva, Inesa, Salem Rabeea Bazar, Oleksii Burhaz, and Olena Vladymyrova. "Methods of comprehensive statistical analysis." Environmental Problems 6, no. 3 (2021): 130–34. http://dx.doi.org/10.23939/ep2021.03.130.

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The article presents an algorithm of a complicated statistical analysis of atmospheric air pollution over certain urban territories, based on methods of multivariate statistical analysis and a statistical decisions theory. Statistical analysis of the concentrations obtained from the observation posts network provides the data on the atmospheric air pollution background characteristics. This analysis enables us to find the spatial correlations between the concentrations of the ingredient under various synoptic conditions. The proposed set of statistical methods is advisable to be applied in the
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15

Loyeva, Inesa, Salem Rabeea Bazar, Oleksii Burhaz, and Olena Vladymyrova. "Methods of comprehensive statistical analysis." Environmental Problems 6, no. 3 (2021): 130–34. http://dx.doi.org/10.23939/ep2021.03.130.

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The article presents an algorithm of a complicated statistical analysis of atmospheric air pollution over certain urban territories, based on methods of multivariate statistical analysis and a statistical decisions theory. Statistical analysis of the concentrations obtained from the observation posts network provides the data on the atmospheric air pollution background characteristics. This analysis enables us to find the spatial correlations between the concentrations of the ingredient under various synoptic conditions. The proposed set of statistical methods is advisable to be applied in the
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16

Jeske, Daniel R., Nathaniel T. Stevens, Alexander G. Tartakovsky, and James D. Wilson. "Statistical methods for network surveillance." Applied Stochastic Models in Business and Industry 34, no. 4 (2018): 425–45. http://dx.doi.org/10.1002/asmb.2326.

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17

D'Esposito, Maria Rosaria, and Michel Tenenhaus. "Statistical methods in performance analysis." Applied Stochastic Models in Business and Industry 24, no. 5 (2008): 369–71. http://dx.doi.org/10.1002/asmb.723.

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18

Mudge, Stephen M. "Multivariate Statistical Methods in Environmental Forensics." Environmental Forensics 8, no. 1-2 (2007): 155–63. http://dx.doi.org/10.1080/15275920601180693.

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19

McBride, Graham B. "Statistical methods helping and hindering environmental science and management." Journal of Agricultural, Biological, and Environmental Statistics 7, no. 3 (2002): 300–305. http://dx.doi.org/10.1198/108571102258.

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20

Anderson, Elizabeth A. "Judgmental and statistical methods of peak electric load management." International Journal of Forecasting 11, no. 2 (1995): 295–305. http://dx.doi.org/10.1016/0169-2070(95)00589-i.

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21

Miller, M., A. Banerjee, G. Christensen, et al. "Statistical methods in computational anatomy." Statistical Methods in Medical Research 6, no. 3 (1997): 267–99. http://dx.doi.org/10.1191/096228097673360480.

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22

Sham, P. C. "Statistical methods in psychiatric genetics." Statistical Methods in Medical Research 7, no. 3 (1998): 279–300. http://dx.doi.org/10.1191/096228098677382724.

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23

Aarons, L. "Statistical methods in laboratory medicine." Statistical Methods in Medical Research 8, no. 3 (1999): 181–82. http://dx.doi.org/10.1191/096228099666030969.

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24

Sham, Pak C. "Statistical methods in psychiatric genetics." Statistical Methods in Medical Research 7, no. 3 (1998): 279–300. http://dx.doi.org/10.1177/096228029800700305.

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25

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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26

Edrysheva, Ekaterina V. "Statistical methods of forecasting legal phenomena." ACCOUNTING AND CONTROL 4 (2024): 7–11. http://dx.doi.org/10.36871/u.i.k.2024.04.01.002.

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The article reveals the issues of forecasting legal phenomena, which helps to develop legislation, i.e. to anticipate potential changes in society and adapt legal norms to changing circumstances. Statistical forecasting methods play a key role in modern legal society. They not only contribute to improving the effectiveness of law enforcement, but also help to prevent crime and improve the management of legal processes. Thanks to statistical analysis, it is possible to make our society safer and more secure, preventing possible threats and negative consequences for citizens.
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27

McCall, Chester H. "Statistical Methods: The Geometric Approach." Journal of Quality Technology 25, no. 1 (1993): 74–75. http://dx.doi.org/10.1080/00224065.1993.11979428.

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28

Glinscaya, Anna, Vladislav Kukartsev, Marina Stepantsevich, Svetlana Pchelintseva, and Mikhail Nikanorov. "Soil quality analysis using statistical methods." BIO Web of Conferences 130 (2024): 02010. http://dx.doi.org/10.1051/bioconf/202413002010.

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This paper presents an analysis of soil quality using statistical methods. The study includes correlation analysis to identify key relationships between various soil and environmental parameters, and regression analysis to quantify the influence of these factors. The findings demonstrate how various factors such as geographical and climatic conditions affect soil quality parameters. The findings of the study highlight the effectiveness of integrating statistical methods for more accurate and reliable analysis of soil quality related data. These results can be useful for developing land managem
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29

Davies, Huw T. O., and T. P. Hutchinson. "Essentials of Statistical Methods, in 41 Pages." Journal of the Operational Research Society 45, no. 5 (1994): 601. http://dx.doi.org/10.2307/2584402.

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30

Davies, Huw T. O. "Essentials of Statistical Methods, in 41 Pages." Journal of the Operational Research Society 45, no. 5 (1994): 601–2. http://dx.doi.org/10.1057/jors.1994.94.

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31

Vasilyeva, Yana Aleksandrovna. "Risk Management: Classification and Management Methods." Общество: политика, экономика право, no. 12 (December 18, 2020): 106–10. http://dx.doi.org/10.24158/pep.2020.12.17.

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The paper contains research of existing risk classifi-cations. The new classification based on this re-search is developed. This classification lets to de-velop effective risk management system in an organ-ization. Suggested classification assumes the selec-tion of three risk types (global, context, system) which demand different management approaches. The manageability of global risks is low, it implies a situational approach to management and an influ-ence of the subjective characteristics of the deci-sion-maker. Context risks require more management resources; therefore, various methods of
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32

Rubinstein, Reuven. "Modern statistical methods in digital simulation." European Journal of Operational Research 26, no. 1 (1986): 178. http://dx.doi.org/10.1016/0377-2217(86)90175-x.

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33

Ermakova, E. V. "Trade credit management in wholesale companies based on statistical methods." Statistics and Economics 15, no. 5 (2018): 27–39. http://dx.doi.org/10.21686/2500-3925-2018-5-27-39.

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Study purpose. The paper shows the application of statistical methods for the trade credit management in the wholesale Russian companies. In this industry, the companies deal with a huge amount of customers, while trade credit is a common practice. As a result, fast and reasonable choice of trade credit terms becomes especially important for wholesale companies. The main study purpose is to provide the methods to choose the trade credit terms.Materials and methods. In this paper, the methods for trade credit management are based of the empirical research where binomial logistic model and discr
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34

Neidert, Sharon, Irwin Miller, and Marylees Miller. "Statistical Methods for Quality: With Application to Engineering and Management." American Statistician 50, no. 2 (1996): 195. http://dx.doi.org/10.2307/2684443.

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35

Wyckoff, James. "Statistical Methods for Quality: With Applications to Engineering and Management." Technometrics 38, no. 2 (1996): 183. http://dx.doi.org/10.1080/00401706.1996.10484466.

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36

ROLSTÅDAS, ASBHØRN. "Editorial The application of statistical methods." Production Planning & Control 5, no. 6 (1994): 511. http://dx.doi.org/10.1080/09537289408919524.

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37

Groeneweg, Jacco, Sofia Caires, and Kathryn Roscoe. "TEMPORAL AND SPATIAL EVOLUTION OF EXTREME EVENTS." Coastal Engineering Proceedings 1, no. 33 (2012): 9. http://dx.doi.org/10.9753/icce.v33.management.9.

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In this study two statistical methods for computing time- and space-evolving extreme events are implemented and assessed. The implementation and application of the methods are carried out with a view towards the safety assessment of water defences. With this type of application in mind, the methods are used to produce time- and space-varying extreme wind fields, which are needed to force wave and hydrodynamic models. Furthermore, the results of the two methods are compared and effects of the choice of distribution, threshold and reference location on
 the resulting wind fields are assesse
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38

Conti, Marcelo Enrique, Marta Iacobucci, Domenico Cucina, and Mauro Mecozzi. "Multivariate statistical methods applied to biomonitoring studies." International Journal of Environment and Pollution 29, no. 1/2/3 (2007): 333. http://dx.doi.org/10.1504/ijep.2007.012809.

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39

Neto, J., Francisco Ferreira, Pedro M. Torres, and F. Boavida. "Lisbon air quality forecast using statistical methods." International Journal of Environment and Pollution 39, no. 3/4 (2009): 333. http://dx.doi.org/10.1504/ijep.2009.028695.

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40

Wang, Min, Ke Fu, Fan Min, and Xiuyi Jia. "Active learning through label error statistical methods." Knowledge-Based Systems 189 (February 2020): 105140. http://dx.doi.org/10.1016/j.knosys.2019.105140.

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41

Holford, T. R. "Statistical methods in genetic epidemiology -- I." Statistical Methods in Medical Research 9, no. 6 (2000): 525–2802. http://dx.doi.org/10.1191/096228000672148636.

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42

Holford, T. R. "Statistical methods in genetic epidemiology -- II." Statistical Methods in Medical Research 10, no. 1 (2001): 1–2802. http://dx.doi.org/10.1191/096228001670140950.

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43

Racine-Poon, A., and J. Wakefield. "Statistical methods for population pharmacokinetic modelling." Statistical Methods in Medical Research 7, no. 1 (1998): 63–84. http://dx.doi.org/10.1191/096228098670696372.

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44

Dunn, G. "Editorial Statistical methods in laboratory medicine." Statistical Methods in Medical Research 8, no. 2 (1999): 91. http://dx.doi.org/10.1191/096228099677518380.

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45

Yang, Kai. "Multivariate statistical methods and Six-Sigma." International Journal of Six Sigma and Competitive Advantage 1, no. 1 (2004): 76. http://dx.doi.org/10.1504/ijssca.2004.005279.

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46

SOLOMCHUK, L.M. "Statistical methods of forecasting results of enterprises." Market Relations Development in Ukraine №6(253)2022 167 (October 25, 2022): 146–51. https://doi.org/10.5281/zenodo.7249868.

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Relevance of the research topic. One of the most popular measures for forecasting the dynamics of socio–economic, financial, technological and technical phenomena and processes is the construction of models based on statistical data and on the basis of which forecast estimates are calculated. Today, in the specialized literature, a large number of forecasting methods for both linear and non–linear processes are described, which can be grouped into three groups. At the same time, there is no systematic approach to choosing the structure of forecasting methods, as well as recommendat
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47

Mabbett, Alan, Donald L. Harnett, and Ashok K. Soni. "Statistical Methods for Business and Economics (4th Edition)." Journal of the Operational Research Society 43, no. 9 (1992): 921. http://dx.doi.org/10.2307/2583295.

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48

Mabbett, Alan. "Statistical Methods for Business and Economics (4th Edition)." Journal of the Operational Research Society 43, no. 9 (1993): 921–22. http://dx.doi.org/10.1057/jors.1992.133.

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49

Stehno, Abigail L., Jeffrey A. Melby, Victor M. Gonzalez, et al. "MULTIVARIATE COASTAL HAZARD RESPONSE STOCHASTIC SIMULATION." Coastal Engineering Proceedings, no. 38 (May 29, 2025): 218. https://doi.org/10.9753/icce.v38.management.218.

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Coastal structure risk analysis is sensitive to underlying physics and statistics. In post-Katrina U.S. studies, it has become increasingly apparent that statistical fidelity can be as important as physical fidelity to evaluate coastal structure responses to storm hazards as part of risk assessments. Fully probabilistic level III reliability techniques yield highly accurate results while also incorporating both aleatory and epistemic uncertainties. Novice application of these methods may seem intimidating; however, the StormSim tool suite eases the use of stochastic simulation for evaluation o
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50

Bhavika, Madnani. "Statistical Methods Quantitative Techniques in Business and Industry." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 1099–105. https://doi.org/10.31142/ijtsrd17041.

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The researcher has done the research on Statistical Methods Quantitative Techniques in Business and Industry.Statistical methods means analyzing, collecting and overall summarization and interpretation of numerical data. Statistical techniques can be used in various fields such as economics, physical sciences and cultural sciences, random phenomena such as radioactivity or meteorological events. This research contains the importance of statistical methods in Business and Industry as well as Classification of Quantitative Techniques, Statistical Quantitative Techniques like Collection of Data M
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