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Journal articles on the topic 'Statistical data processing'

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1

OHE, Shuzo. "Statistical Data Processing." Journal of the Japan Society of Colour Material 67, no. 9 (1994): 590–95. http://dx.doi.org/10.4011/shikizai1937.67.590.

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MILOSAN, Ioan. "STATISTICAL PROCESSING OF EXPERIMENTAL DATA USING ANALYSIS OF VARIANCE." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, no. 1 (2016): 489–96. http://dx.doi.org/10.19062/2247-3173.2016.18.1.67.

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3

Šķiltere, Daina, and Svetlana Jesiļevska. "Data quality evaluation in statistical data processing." Statistical Journal of the IAOS: Journal of the International Association for Official Statistics 30, no. 4 (2014): 425–30. https://doi.org/10.3233/sji-140806.

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National Statistical Offices (NSO's) need data for the production of statistics. Apart from data obtained through surveys, NSO's are increasingly using other data sources (for example, data collected by other organizations). Disadvantages here come from the fact that very often an NSO uses an administrative data source for a purpose different than the one for which the data was originally collected. As a result of this difference, the 'statistical' usability of a data source needs to be thoroughly studied by an NSO before its use. Some more possible problems of statistics are the following: da
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4

Neubauer, Jiří, Oldřich Kříž, and Marek Sedlačík. "Statistical Data Processing in STAT1 Application." Vojenské rozhledy 22, no. 3 (2013): 114–22. http://dx.doi.org/10.3849/2336-2995.22.2013.03.114-122.

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5

Smit, Suzanne, Huub C. J. Hoefsloot, and Age K. Smilde. "Statistical data processing in clinical proteomics." Journal of Chromatography B 866, no. 1-2 (2008): 77–88. http://dx.doi.org/10.1016/j.jchromb.2007.10.042.

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6

Shermukhamedov, A. T., and Yo Ilkhamova. "STATISTICAL DATA PROCESSING IN THE DIGITAL ECONOMY." Theoretical & Applied Science 94, no. 02 (2021): 177–79. http://dx.doi.org/10.15863/tas.2021.02.94.41.

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Vidal, David, and Annio Moreno. "Statistical parameter flexibility for processing navigation data." Leading Edge 29, no. 4 (2010): 414–16. http://dx.doi.org/10.1190/1.3378305.

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8

Pankov, A. R., and A. M. Skuridin. "Data Processing Under A Priori Statistical Uncertainty." IFAC Proceedings Volumes 19, no. 5 (1986): 213–17. http://dx.doi.org/10.1016/s1474-6670(17)59796-7.

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Atzeni, Paolo, Luigi Bellomarini, Francesca Bugiotti, and Marco De Leonardis. "Executable schema mappings for statistical data processing." Distributed and Parallel Databases 36, no. 2 (2017): 265–300. http://dx.doi.org/10.1007/s10619-017-7212-2.

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10

Norin, Veniamin, and Yurii Pukharenko. "Statistical processing of traffic flow characteristics data." E3S Web of Conferences 371 (2023): 04031. http://dx.doi.org/10.1051/e3sconf/202337104031.

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In the course of statistical processing of traffic flows characteristics data, the check for the presence of anomalous measurements in the sampling should be done at the very start of processing. If anomalous measurements are detected, they should be excluded from the sampling at an early stage of the processing and not taken into considerations in further calculations. Numerous criteria have been developed to detect outliers, their effectiveness depends on the sample size. In practice, for technical and economic reasons, it is impractical to obtain a large number of measurements, as a rule th
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11

Gevorkyan, Migran N., Anna V. Korolkova, and Dmitry S. Kulyabov. "Julia language features for processing statistical data." Discrete and Continuous Models and Applied Computational Science 31, no. 1 (2023): 5–26. http://dx.doi.org/10.22363/2658-4670-2023-31-1-5-26.

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The Julia programming language is a specialized language for scientific computing. It is relatively new, so most of the libraries for it are in the active development stage. In this article, the authors consider the possibilities of the language in the field of mathematical statistics. Special emphasis is placed on the technical component, in particular, the process of installing and configuring the software environment is described in detail. Since users of the Julia language are often not professional programmers, technical issues in setting up the software environment can cause difficulties
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12

Radchuk, O. D., and A. V. Makhinko. "TAILINGS DAM FAILURES: A STATISTICAL DATA ANALYSIS." Modern structures of metal and wood, no. 28 (August 2024): 183–91. http://dx.doi.org/10.31650/2707-3068-2024-28-183-191.

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Minerals processing produces two main products: a commercial product (concentrate, pellets) and tailings (waste from ore processing). The tailings are stored in a tailings storage facility (TSF), a special hydraulic structure for receiving and storing solid and liquid mineral processing waste. The focus is primarily on ensuring the reliability and safety of these facilities during their design, construction, and operation, as a failure of such structures can have catastrophic consequences.
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13

Yordanova, Lina, Gabriela Kiryakova, Petya Veleva, Nadezhda Angelova, and Antoaneta Yordanova. "Criteria for selection of statistical data processing software." IOP Conference Series: Materials Science and Engineering 1031, no. 1 (2021): 012067. http://dx.doi.org/10.1088/1757-899x/1031/1/012067.

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14

S. Al-Anzi, Fawaz, and DiaAbuZeina. "Statistical Markovian Data Modeling for Natural Language Processing." International Journal of Data Mining & Knowledge Management Process 7, no. 1 (2017): 25–35. http://dx.doi.org/10.5121/ijdkp.2017.7103.

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15

Becker, Roger J. "Statistical processing of multiplexed data from turbulent flames." AIAA Journal 23, no. 8 (1985): 1288–91. http://dx.doi.org/10.2514/3.9081.

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16

Sugiyama, Masashi, Takafumi Kanamori, Taiji Suzuki, et al. "A Density-ratio Framework for Statistical Data Processing." IPSJ Transactions on Computer Vision and Applications 1 (2009): 183–208. http://dx.doi.org/10.2197/ipsjtcva.1.183.

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17

Anastasya, Diva Berta, and Imam Yuadi. "BIBLIOMETRIC ANALYSIS OF STATISTICAL DATA PROCESSING USING SPSS." MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) 16, no. 2 (2024): 97–107. http://dx.doi.org/10.18860/mat.v16i2.25656.

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Background: This research is related to statistical data processing using SPSS (Statistical Package for Social Science). Statistics has several methods that can be used in research, especially in statistical data processing. One of the tools used for quantitative data analysis is SPSS. This article focuses on bibliometric analysis in determining the data to be reviewed. Purpose: Analyze "Statistical Data Processing Using SPSS" through a search on the Web Of Science. Method: Data search is done on Web Of Science, then bibliometric analysis is done using Biblioshiny. Result: The final result thr
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18

ПАХОМОВА, Т. В., Л. А. ВОЛОЩУК, В. А. ШИБАЙКИН, and Д. Н. ГИЛЯЖЕВА. "STATISTICAL DATA PROCESSING USING CORRELATION AND REGRESSION ANALYSIS." Экономика и предпринимательство, no. 9(158) (November 18, 2023): 1192–95. http://dx.doi.org/10.34925/eip.2023.158.09.231.

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Обработка статистических данных уже давно применяется в самых разнообразных видах человеческой деятельности. Вообще говоря, трудно назвать ту сферу, в которой она бы не использовалась. Но, пожалуй, ни в одной области знаний и практической деятельности обработка статистических данных не играет такой исключительно большой роли, как в экономике, имеющей дело с обработкой и анализом огромных массивов информации о социально-экономических явлениях и процессах. Всесторонний и глубокий анализ этой информации, так называемых статистических данных, предполагает использование различных специальных методо
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19

Sadowsky, George. "Statistical data processing in developing countries: Infrastructure considerations." Computers, Environment and Urban Systems 13, no. 4 (1989): 269–89. http://dx.doi.org/10.1016/0198-9715(89)90052-5.

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20

Keller, Wouter J., and Jelke Bethlehem. "The impact of EDI on statistical data processing." Computational Statistics 15, no. 1 (2000): 53–63. http://dx.doi.org/10.1007/s001800050036.

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21

Mahariani, Yeni Roha, Pangki Suseno, and Muhammad Ikhwan Febriansyah. "Training on Statistical Data Processing Using SPSS Application." Mattawang: Jurnal Pengabdian Masyarakat 4, no. 4 (2023): 377–81. http://dx.doi.org/10.35877/454ri.mattawang2252.

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The Industry 4.0 era requires resources equipped with critical thinking, innovative abilities, and the capacity to address complex problems using the latest technology. Education emerges as a crucial factor in meeting Human Resources (HR) needs across various industrial sectors. Therefore, this community service activity focuses on students to enhance insights and competencies that support lectures and research projects. The training places emphasis on utilizing the Statistical Package for the Social Sciences (SPSS) as a statistical testing tool, enabling students to grasp accurate data proces
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22

Khalilov, A. T., K. S. Ishenova, and A. A. Kambarov. "METHODS FOR PROCESSING STATISTICAL DATA USING MICROSOFT EXCEL." Vestnik Bishkek state university af K Karasaev 71, no. 1 (2025): 94–103. https://doi.org/10.35254/bsu/2025.71.15.

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This article examines statistical data processing methods using Microsoft Excel's “Data Analysis” package and “Regression” tool. This study analyzes two-factor statistical indicators and the in-depth relationship between dependent and independent factors. This study applied correlation and regression analyses to medical data to explore the connection between the heart rate and breathing rate. The results show that Excel tools are more effective than calculators or individual-program operators for statistical analysis. The methodology benefits specialists working in various fields with statisti
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23

Werness, S. "Statistical evaluation of predictive data compression systems." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 8 (1987): 1190–98. http://dx.doi.org/10.1109/tassp.1987.1165268.

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24

Parnovsky, S. "Some problems in the statistical processing of astronomic data." Bulletin of Taras Shevchenko National University of Kyiv. Astronomy, no. 59 (2019): 34–36. http://dx.doi.org/10.17721/btsnua.2019.59.34-36.

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The problems that could arise with a correct statistical processing of astronomical data and a search for linear statistical dependences are analyzed. It has been shown that the use of standard statistical software can yield incorrect results. The iterative procedure of processing based on multi-year experience is proposed. It covers all stages: creation of sampling, discarding of outliers, search for statistically significant regressors, excluding insignificant regressors, finding out multiparametric dependencies and their analysis, etc. This paper is a methodological one and is it describes
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25

Edwards, Katherine M., and Lynn M. Hastie. "Processing Magnetotelluric Data With Modern Statistical and Numerical Techniques." Exploration Geophysics 28, no. 1-2 (1997): 43–47. http://dx.doi.org/10.1071/eg997043.

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26

Kasyanov, Valeriy, Evgeny Kosenko, Vera Kosenko, and Vasily Krymsky. "Method for processing experimental strength data using statistical methods." E3S Web of Conferences 157 (2020): 02019. http://dx.doi.org/10.1051/e3sconf/202015702019.

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The selective calculation method has been successfully applied for several centuries due to its profitability. However, when calculating the failure-freeness in the case of fatigue fractures (often sudden), when there is a significant power relationship m = 10-15 for the super-cycle loading region between the strength (hardness, endurance limit) and the effective voltage, the reduction of the minimum component life will be 10-100 or more times. This is due to the unaccounted main part of the population. In our example, there are 104 50 = 9950 units of such parts, i.e., their potential failures
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27

Kosimov, Kozimjon, and Gulnoza Karimova. "STATISTICAL PROCESSING OF EMPIRICAL DATA IN THE RESEARCH PROCESS." International Journal of Business, Law and Political Science 1, no. 4 (2024): 77–82. http://dx.doi.org/10.61796/ijblps.v1i4.92.

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Based on preliminary analysis on the object of empirical Sociological Research, an existing problem analysis program is developed. The scientific program is the first necessary document of any practical, theoretical sociological study. The program is aimed at checking the working hypothesis on the object of sociological research, and also consists of the statements of the scientific hypothesis on research, the main tasks to be performed and the methodological basis of sociological research. This paper scientifically investigated the process of statistical processing of empirical data.
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28

Basseville, Michèle. "Divergence measures for statistical data processing—An annotated bibliography." Signal Processing 93, no. 4 (2013): 621–33. http://dx.doi.org/10.1016/j.sigpro.2012.09.003.

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29

Sigorskii, P. V. "Statistical characteristics of an integrated oceanographic data processing system." Journal of Soviet Mathematics 63, no. 5 (1993): 598–603. http://dx.doi.org/10.1007/bf01142537.

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30

Shin, Sung-Hwan, Jeong-Guon Ih, and Hyuk Jeong. "Statistical processing of subjective listening test data for PSQ." Noise Control Engineering Journal 51, no. 4 (2003): 232. http://dx.doi.org/10.3397/1.2839718.

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31

Lin, Bing-Rong, and Daniel Kifer. "Information Measures in Statistical Privacy and Data Processing Applications." ACM Transactions on Knowledge Discovery from Data 9, no. 4 (2015): 1–29. http://dx.doi.org/10.1145/2700407.

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32

Antti, H., M. E. Bollard, T. Ebbels, et al. "Batch statistical processing of1H NMR-derived urinary spectral data." Journal of Chemometrics 16, no. 8-10 (2002): 461–68. http://dx.doi.org/10.1002/cem.733.

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33

Lebart, Ludovic, André Salem, and Lisette Berry. "Recent developments in the statistical processing of textual data." Applied Stochastic Models and Data Analysis 7, no. 1 (1991): 47–62. http://dx.doi.org/10.1002/asm.3150070106.

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34

M.Mukhitdinova. "THEORETICAL ASPECTS OF MACHINE LEARNING IN STATISTICAL DATA PROCESSING." Role of agriculture and medicine in science va O'zbekiston Agrar fani xabarnomasi Maxsus son, no. 1/1(19) 2025 (2025): 272–75. https://doi.org/10.5281/zenodo.15020405.

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35

Dudley, R. A., P. Edwards, R. P. Ekins, et al. "Guidelines for immunoassay data processing." Clinical Chemistry 31, no. 8 (1985): 1264–71. http://dx.doi.org/10.1093/clinchem/31.8.1264.

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Abstract These guidelines outline the minimum requirements for a data-processing package to be used in the immunoassay laboratory. They include recommendations on hardware, software, and program design. We outline the statistical analyses that should be performed to obtain the analyte concentrations of unknown specimens and to ensure adequate monitoring of within- and between-assay errors of measurement.
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36

GOLANOVA, ANNA V., EKATERINA I. GOLIKOVA, and ILYA A. TREGUBENKO. "SELECTION OF STATISTICAL METHODS FOR PROCESSING DATA OF A PEDAGOGICAL EXPERIMENT." Cherepovets State University Bulletin 5, no. 104 (2021): 167–78. http://dx.doi.org/10.23859/1994-0637-2021-5-104-14.

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The article deals with the issue of statistical methods for processing data obtained as a result of a pedagogical experiment. The authors carry out the analysis of sources on statistical processing of pedagogical data and consider the stages of data processing in the pedagogical experiment. A general algorithm for the selection of statistical methods for data processing in an experiment is described and the principles that should be taken into account when implementing this algorithm are presented. The authors give an example of statistical processing by reference to a specific task.
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37

Kuzminets, Nikolai, and Yurii Dubovenko. "Constraints of optimization of statistical analysis of data of engineerig monitoring of transport networks." Automobile Roads and Road Construction, no. 109 (2021): 157–65. http://dx.doi.org/10.33744/0365-8171-2021-109-157-165.

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Time series for the technical monitoring of the transportation networks include interference and omissions. Their analysis requires the special statistical analysis. Known statistical packages do not contain a full cycle for processing of large time series. The linear timeline for the processing of periodic data is not available in digital statistics. The sliding window approach is suitable for processing of interrupted time series. Its disadvantage is the restriction on the length of the row and the sensitivity to data gaps. The graph of the time series process­ing needs the internal optimiza
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38

OSAULENKO, O., and O. HOROBETS. "Social Media Data in the Big Data Environment." Scientific Bulletin of the National Academy of Statistics, Accounting and Audit, no. 3-4 (December 21, 2021): 23–31. http://dx.doi.org/10.31767/nasoa.3-4-2021.03.

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The article contains results of a study of social media data (SMD) which, being distinct from conventional data by their origin, require special methods for collection, processing and analysis. As shown by a literature review, in spite of great many research publications devoted to social media research and big data analysis, the SMD potential as a big data component still remains inadequately explored. 
 Two approaches to research and analysis of SMD were highlighted in course of the study, in which SMD are addressed as an object of Internet statistics and an object of big data. When SMD
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39

Grabusts, Pēteris. "APPLICATION POSSIBILITIES OF ASSOCIATION RULES IN STATISTICAL DATA ANALYSIS." Latgale National Economy Research 1, no. 6 (2014): 77. http://dx.doi.org/10.17770/lner2014vol1.6.1168.

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This paper studies one of intelligent data processing methods: using association rules for data analysis. The method of association rule obtaining what was initially developed to analyse consumer’s basket has turned to be a good tool for other tasks too. The method helps search and find regularities of the form X  Y in different kinds of data. Nowadays this method is widely applied in the tasks of large scale database processing and analysing. As a result, methods of association rule construction occupy their place among the basic methods of intelligent data processing. The paper consists of
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40

Matanat Hasanguliyeva, Matanat Hasanguliyeva, and Sabina Orujova Sabina Orujova. "BIG DATA AND STATISTICAL APPLICATIONS." ETM - Equipment, Technologies, Materials 23, no. 05 (2024): 68–73. http://dx.doi.org/10.36962/etm23052024-07.

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In recent years, modern developments in computer and cloud technologies have led to a significant increase in the quantity and speed of generated and stored information. This increase in the amount of information has introduced a new concept into our lives called "Big Data". Big Data offers significant advantages, especially for governments and businesses, in making informed decisions about their futures. However, the magnitude and diversity of this emerging data also bring about some challenges. Traditional database systems have been inadequate in processing these data formats, necessitating
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41

Outrata, Edvard, and Ed Doucet. "Impact of new processing techniques on the management and organization of statistical data processing." Statistical Journal of the United Nations Economic Commission for Europe 5, no. 2 (1988): 201–10. http://dx.doi.org/10.3233/sju-1988-5208.

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42

Pisareva, D. A., E. A. Pakhomova, and O. V. Rozhkova. "Methodological Approaches to Data Pre-Processing Formalization for Statistical Analysis." IOP Conference Series: Materials Science and Engineering 1099, no. 1 (2021): 012022. http://dx.doi.org/10.1088/1757-899x/1099/1/012022.

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43

Moshchenko, I., O. Nikitenko, Yu Kozlov, and Yu Zharko. "Feature of statistical data processing by computer mathematics systems tools." Radiotekhnika, no. 206 (September 24, 2021): 131–36. http://dx.doi.org/10.30837/rt.2021.3.206.12.

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Processes of error accumulation were analysed during arithmetic operations on statistical data obtained in the course of research on oscillations in cross-field electron vacuum devices.
 The features of statistical data processing obtained as a result of experimental research were investigated using the most widespread computer mathematical packages.
 The features of statistical data processing were investigated by processing a sample of 80 values of the magnetron generation frequency using popular mathematical packages Excel, Maple, Matlab and MathCad and comparing the results obtai
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44

Kalinchik, Vasilij, Vitaliy Pobigaylo, Vitaliy Kalinchyk, and Daniil Filjanin. "Use of statistical analysis methods in processing population survey data." Bulletin of NTU "KhPI". Series: Problems of Electrical Machines and Apparatus Perfection. The Theory and Practice, no. 1 (5) (May 28, 2021): 58–60. http://dx.doi.org/10.20998/2079-3944.2021.1.11.

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The article analyzes the structures of the measuring channels of electricity metering systems. It is shown that such a structure is determined by the types of measuring instruments used and the scheme of their connection, in particular, by the types of electricity meters installed at the metering points. Moreover, the counters can have a pulse or interface information output. It is shown that the path for measuring and metering electricity includes a measuring circuit consisting of measuring current and voltage transformers, an electricity meter, communication lines, a metering device and a da
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45

Hinze, Kenneth. "Begin Courses with Data Analysis Using SPS Statistical Processing System." Social Science Microcomputer Review 4, no. 3 (1986): 375–78. http://dx.doi.org/10.1177/089443938600400310.

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Швецов, Ярослав, Yaroslav Shvetsov, Владимир Аверченков, et al. "INTELLIGENT DATA PROCESSING IN PROBLEM OF ECONOMIC STATISTICAL INFORMATION SYSTEMATIZATION." Bulletin of Bryansk state technical university 2017, no. 8 (2017): 55–62. http://dx.doi.org/10.12737/article_5a3779fddad627.59611142.

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47

Van De Ville, Dimitri, Thierry Blu, and Michael Unser. "Integrated wavelet processing and spatial statistical testing of fMRI data." NeuroImage 23, no. 4 (2004): 1472–85. http://dx.doi.org/10.1016/j.neuroimage.2004.07.056.

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48

MUTO, Shunsuke, and Motoki SHIGA. "Application of Statistical/Information Processing to Spectral Image Data Analysis." Hyomen Kagaku 37, no. 12 (2016): 610–15. http://dx.doi.org/10.1380/jsssj.37.610.

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Šlampová, Andrea, and Petr Boček. "Statistical data processing of mobility curves of univalent weak bases." ELECTROPHORESIS 29, no. 2 (2008): 538–41. http://dx.doi.org/10.1002/elps.200700524.

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Muaidy, Yasin1* Masrun2 Akung Daeng3 Helmi Fuadi4 Himawan Sutanto5. "ANALYSIS OF STATISTICAL DATA PROCESSING IN THE DATA PRESENTATION OF THE CENTRAL STATISTICAL AGENCY OF WEST NUSA TENGGARA PROVINCE IN 2021/2022." ISRG Journal of Arts Humanities & Social Sciences (ISRGJAHSS) II, no. III (2024): 273–79. https://doi.org/10.5281/zenodo.11581276.

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<em>This research study is about "Processing analysis in presenting data from the West Nusa Tenggara Province Central Statistics Agency. The aim is to apply statistical data in the form of presenting quantitative data at the Central Statistics Agency (BPS). This research report was created as an illustration of the results of the coordination function of Statistical Processing Integration and Dissemination specifically at the Integrated Service Center (PST). This research uses quantitative data for the 2021/2022 period at the Central Statistics Agency (BPS) of West Nusa Tenggara Province.</em>
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