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Journal articles on the topic 'Design and Data Analysis'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Banks, David L., and Colin L. Mallows. "Design, Data, Analysis." Journal of the American Statistical Association 83, no. 404 (1988): 1214. http://dx.doi.org/10.2307/2290162.

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2

王, 倩. "Analysis of Wardrobe User Needs Based on Data Statistics." Design 09, no. 03 (2024): 846–53. http://dx.doi.org/10.12677/design.2024.93391.

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3

Kemp, A. W., and C. L. Mallows. "Design, Data and Analysis." Biometrics 44, no. 3 (1988): 914. http://dx.doi.org/10.2307/2531608.

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4

Ziegel, Eric. "Design, Data, and Analysis." Technometrics 31, no. 1 (1989): 118–19. http://dx.doi.org/10.1080/00401706.1989.10488488.

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5

Suresh, Geetha, SanjeevV Thomas, and Karthik Suresh. "Design and data analysis 1 study design." Annals of Indian Academy of Neurology 15, no. 2 (2012): 76. http://dx.doi.org/10.4103/0972-2327.94987.

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6

Kumar, Ch Kishore. "Analysis and Design of Data Mining Techniques to Develop Software Reliability." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (2020): 167–73. http://dx.doi.org/10.5373/jardcs/v12i4/20201429.

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7

Nievergelt, J. "Data analysis-the key to data base design." Computer Compacts 4, no. 5 (1986): 173. http://dx.doi.org/10.1016/0167-7136(86)90011-9.

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8

Hidayati, Juliza, Shelvy Riry Gusrina, and Nazaruddin Matondang. "Big data analysis for product design." IOP Conference Series: Materials Science and Engineering 1122, no. 1 (2021): 012064. http://dx.doi.org/10.1088/1757-899x/1122/1/012064.

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9

Abbott, Steve, and Mark Woodward. "Epidemiology: Study Design and Data Analysis." Mathematical Gazette 84, no. 499 (2000): 191. http://dx.doi.org/10.2307/3621565.

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10

Ziegel, Eric R., and Mark Woodward. "Epidemiology: Study Design and Data Analysis." Technometrics 42, no. 3 (2000): 327. http://dx.doi.org/10.2307/1271123.

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11

Callas, Peter. "Epidemiology: Study Design and Data Analysis." Journal of the American Statistical Association 96, no. 453 (2001): 339–55. http://dx.doi.org/10.1198/jasa.2001.s381.

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12

Davies, Simon. "Epidemiology: Study Design and Data Analysis." Journal of the American Statistical Association 101, no. 473 (2006): 404–5. http://dx.doi.org/10.1198/jasa.2006.s92.

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13

Moss, T. R., and J. E. Strutt. "Data Sources for Reliability Design Analysis." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 207, no. 1 (1993): 13–19. http://dx.doi.org/10.1243/pime_proc_1993_207_203_02.

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Quantified reliability assessment is now employed by many organizations in the process industries. The objective is to identify critical areas in the plant where design changes are required to meet the safety or production specification. Mechanical system assessment is, however, frequently inhibited by the lack of representative equipment reliability data. Where these data are not available in-house recourse must be made to generic sources from the public domain. Members of the Process Industries Division's Mechanical Reliability Committee have recently completed a study to identify and evalua

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14

Draper, Norman R. "Data Analysis for Experimental Design by." International Statistical Review 77, no. 1 (2009): 147. http://dx.doi.org/10.1111/j.1751-5823.2009.00074_1.x.

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15

Sayed, A. H., V. H. Nascimento, and F. A. M. Cipparrone. "A Regularized Robust Design Criterion for Uncertain Data." SIAM Journal on Matrix Analysis and Applications 23, no. 4 (2002): 1120–42. http://dx.doi.org/10.1137/s0895479800380799.

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16

Lee, Shin Haeng. "Combining Design- and Model-based Analyses for Cross-national Survey Data." Korean Data Analysis Society 24, no. 3 (2022): 933–42. http://dx.doi.org/10.37727/jkdas.2022.24.3.933.

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Survey weighting matters when different sampling probabilities between countries, as well as within a country, are related to the response variable conditional on its predictors. This study aims at improving estimation of regression coefficients from a cross-national data set by assessing the relative effectiveness of different techniques for survey weighting to gain in precision. Data came from the Asian subset of the World Values Survey. Using pooled data across five Asian countries, the logistic regression analysis is fitted to examine the relationship between information seeking behaviors

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17

Sreekanta Murthy, T., and J. S. Arora. "Data base design methodology for structural analysis and design optimization." Engineering with Computers 1, no. 3 (1986): 149–60. http://dx.doi.org/10.1007/bf01200115.

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18

Yang, Kai, and Ethem Cekecek. "Design Vulnerability Analysis and Design Improvement by Using Warranty Data." Quality and Reliability Engineering International 20, no. 2 (2004): 121–33. http://dx.doi.org/10.1002/qre.617.

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19

Carvalho, Goncalo, Sergii Mykolyshyn, Bruno Cabral, Jorge Bernardino, and Vasco Pereira. "Comparative Analysis of Data Modeling Design Tools." IEEE Access 10 (2022): 3351–65. http://dx.doi.org/10.1109/access.2021.3139071.

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20

DʼAgostino, Ralph B. "Issues of Study Design and Data Analysis." Inflammatory Bowel Diseases 4, no. 2 (1998): 109–13. http://dx.doi.org/10.1097/00054725-199805000-00012.

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21

Pighin, M., and L. Ieronutti. "Quantitative analysis of data warehouse design quality." International Journal of Intelligent Defence Support Systems 3, no. 1/2 (2010): 52. http://dx.doi.org/10.1504/ijidss.2010.033676.

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22

Oliveira, M. C., G. A. M. Pereira, E. A. Ferreira, J. B. Santos, S. Z. Knezevic, and R. Werle. "Additive design: the concept and data analysis." Weed Research 58, no. 5 (2018): 338–47. http://dx.doi.org/10.1111/wre.12317.

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23

Thenkabail, Prasad S. "Hyperspectral Data Processing: Algorithm Design and Analysis." Photogrammetric Engineering & Remote Sensing 81, no. 6 (2015): 441–42. http://dx.doi.org/10.14358/pers.81.6.441.

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24

Reid, Julian R. W. "Experimental Design and Data Analysis for Biologists." Austral Ecology 28, no. 5 (2003): 588–89. http://dx.doi.org/10.1046/j.1442-9993.2003.01318.x.

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25

Petraitis, Peter S. "Experimental Design and Data Analysis for Biologists." Journal of Experimental Marine Biology and Ecology 277, no. 2 (2002): 197–98. http://dx.doi.org/10.1016/s0022-0981(02)00278-2.

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26

Nasir, J. A., M. K. Shahzad ., and M. A. Pasha . "Data Warehouse Design for Sales Performance Analysis." Information Technology Journal 5, no. 5 (2006): 964–69. http://dx.doi.org/10.3923/itj.2006.964.969.

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27

Son, Sung Il, and Chan Khon Park. "Design of Big Data Preference Analysis System." Journal of Korea Multimedia Society 17, no. 11 (2014): 1286–95. http://dx.doi.org/10.9717/kmms.2014.17.11.1286.

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28

Mendez, Gonzalo, Xavier Ochoa, Katherine Chiluiza, and Bram De Wever. "Curricular Design Analysis: A Data-Driven Perspective." Journal of Learning Analytics 1, no. 3 (2014): 84–119. http://dx.doi.org/10.18608/jla.2014.13.6.

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Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of Learning Analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, course impact on t

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29

Adams, Barry J., Hugh G. Fraser, Charles D. D. Howard, and M. Sami Hanafy. "Meteorological Data Analysis for Drainage System Design." Journal of Environmental Engineering 112, no. 5 (1986): 827–48. http://dx.doi.org/10.1061/(asce)0733-9372(1986)112:5(827).

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30

Funkhouser, Kelsey, William M. Martinez, Rachel Henderson, and Marcos D. Caballero. "Design, analysis, tools, and apprenticeship (DATA) Lab." European Journal of Physics 40, no. 6 (2019): 065701. http://dx.doi.org/10.1088/1361-6404/ab2f0d.

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31

Rand, William M. "Experimental design for food composition data analysis." Journal of Food Composition and Analysis 5, no. 4 (1992): 267–68. http://dx.doi.org/10.1016/0889-1575(92)90060-w.

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32

Bloombaum, Milton. "Influence of research design upon data analysis." Quality & Quantity 25, no. 3 (1991): 327–31. http://dx.doi.org/10.1007/bf00167538.

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33

O'Clock, George D., and Donald L. Henderson. "Design and Analysis of Data Communication Systems." International Journal of Network Management 4, no. 3 (1994): 149–57. http://dx.doi.org/10.1002/nem.4560040307.

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34

Keswani, Hrishikesh, Krishit Shah, Hritik Hassani, Moses Gadkar, and Er Manoj Kavedia. "Data Visualization and Analysis of COVID-19 Data." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 1328–37. http://dx.doi.org/10.22214/ijraset.2022.47179.

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Abstract: During the COVID-19 pandemic, many data visualizations were created to alert the public to the rapidly growing threat. Statistics on the spread of COVID-19 have been displayed on data dashboards, a mechanism for sharing information throughout the pandemic, which has aided in this process. When developing the visuals for COVID-19, the majority of time was spent on the technical aspects of designing and evaluating various visualization methods. Little is understood about the inner workings of visualization production processes due to the complex sociotechnical environments in which the

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35

Li, Yan Qin, and Chun Fu Li. "Data Acquisition System Design and Data Analysis on Automatic Transmission." Applied Mechanics and Materials 239-240 (December 2012): 861–64. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.861.

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An efficient data acquisition system for electronic automatic transmission test, which makes use of American NI’s data acquisition card, is designed based on Virtual Instrument. The data can be collected by the data acquisition system include automatic transmission oil pressure, solenoid duty, as well as engine speed ，turbine speed ，output shaft speed and throttle degree，and so on. The key problems of the system design are measurement method of automatic transmission solenoid duty, signal conditioning circuit design and multi-signal acquisition synchronization. Test platform is North-Benz dump

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36

Maryanto, Hendra, Bambang Purnomosidi Dwi Putranto, Rikie Kartadie, Muhammad Guntara, and Robertus Saptoto. "ANALYSIS AND DESIGN OF DATA WAREHOUSE AND DATA MART BUDGET." Journal of Intelligent Software Systems 2, no. 1 (2023): 6. http://dx.doi.org/10.26798/jiss.v2i1.927.

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University as higher education institutions must be able to manage budgets properly. The budget is a future financial plan which includes the expectations of university management. This research will design a data warehouse, which is a place where data can be stored on a large scale. In this research, a data warehouse will be designed as a place to store budget data. The method applied in this study is the Kimball method with a nine-step methodology. The result of this research is a data warehouse design and budget data mart

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37

Ryu, Sun Joo, and Joungmin Kim. "Design thinking trend analysis using big data-based textom analysis." Korean Association For Learner-Centered Curriculum And Instruction 22, no. 20 (2022): 265–81. http://dx.doi.org/10.22251/jlcci.2022.22.20.265.

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Objectives This study aims to investigate the design thinking research trends in Korea and suggest future research directions. To achieve this purpose, a total of 337 papers related to design thinking published since 2000 were collected. Methods Based on the collected research papers, research trends were examined by year and journal status and research methods, and networks between keyword-related words were analyzed through Big Data analysis program Textom and Ucinet6 NetDraw functions. visualized. In addition, CONCOR analysis was performed to derive similar clusters of derived words. Result

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38

F, Ryan, and Fitrah R. "Applications Data Warehouse for Sales Analysis Based on Multidimensional Modeling (MDM) and Star Schema Design." International Journal of Psychosocial Rehabilitation 24, no. 02 (2020): 3927–34. http://dx.doi.org/10.37200/ijpr/v24i2/pr200711.

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39

Al-Aita, Abdulrahman, Muhammad Aslam, and Rafif Al-Habib. "Analysis of Sudoku square designs for imprecise data." Journal of Statistics and Management Systems 28, no. 4 (2025): 741–60. https://doi.org/10.47974/jsms-1373.

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The classic Sudoku square design is applied for testing the null hypothesis when all observations in the data are precise and certain. In some cases, it is difficult for researchers to obtain exact observations due to circumstances beyond their control. Therefore, the observations may be imprecise and noted in intervals. We must therefore search for a suitable generalization of Sudoku square designs in uncertain environments. This paper aims to introduce the four various statistical models of the Sudoku square design under neutrosophic statistics, which provides a solution to the problem of im

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40

Arner, Stanford L., James A. Westfall, and Charles T. Scott. "Comparison of Annual Inventory Designs Using Forest Inventory and Analysis Data." Forest Science 50, no. 2 (2004): 188–203. http://dx.doi.org/10.1093/forestscience/50.2.188.

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Abstract Three annual inventory designs, a periodic design, and a periodic measurement with midcycle update design are compared using a population created from 14,754 remeasured Forest Inventory and Analysis plots. Two of the annual designs and the midcycle update design allow updating of plots not measured using sampling with partial replacement procedures. Comparisons are based on root mean square error and estimator bias determined for net volume (m3/hectare) and mean annual net volume change. For the annual designs, both individual year and moving average estimates are investigated. The la

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41

Ryan, Steven. "Linear data flow analysis." ACM SIGPLAN Notices 27, no. 4 (1992): 59–67. http://dx.doi.org/10.1145/131080.131088.

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42

Ramalingam, G. "Data flow frequency analysis." ACM SIGPLAN Notices 31, no. 5 (1996): 267–77. http://dx.doi.org/10.1145/249069.231433.

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43

Khedker, Uday P., and Dhananjay M. Dhamdhere. "Bidirectional data flow analysis." ACM SIGPLAN Notices 34, no. 6 (1999): 47–57. http://dx.doi.org/10.1145/606666.606676.

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44

Sharma, Mr Love. "Design of Secured Ground Vehicle Event Data Recorder for Data Analysis." International Journal of Engineering Research and Applications 07, no. 03 (2017): 37–48. http://dx.doi.org/10.9790/9622-0703053748.

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45

Pineau, Nicolas, Mireille Moser, Fabien Rawyler, Mélissa Lepage, Nicolas Antille, and Andreas Rytz. "Design of experiment with sensory data: A pragmatic data analysis approach." Journal of Sensory Studies 34, no. 2 (2019): e12489. http://dx.doi.org/10.1111/joss.12489.

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46

Tan, Weiyan. "Design of Vehicle Big Data Analysis System Based on Structured Data." Science, Technology and Social Development Proceedings Series 2 (March 21, 2025): 244–51. https://doi.org/10.70088/e7zt6046.

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In order to meet the needs of information construction of company and make full use of the increasingly large volume of traffic checkpoint data, a big data analysis system of vehicles based on structured data has been designed and implemented. The checkpoint data provided by the video structured platform was utilized by the system, which employed Kafka as the message bus, Elasticsearch as the distributed platform for storage and search, Redis as the distributed service for model and algorithm, and a warehouse for the real-time transmission, storage, retrieval, and analysis of traffic data. The

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47

Zhang, Huan Cheng, Ya Feng Yang, Ai Min Yang, and Li Nan Shi. "Data Envelopment Analysis Model of Mechanical Product Design." Applied Mechanics and Materials 443 (October 2013): 58–61. http://dx.doi.org/10.4028/www.scientific.net/amm.443.58.

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For the existing problems in the design of mechanical products, based on the theory of fuzzy mathematics, the paper constructs fuzzy comprehensive evaluation model to comprehensively evaluate the existing problems. Then, by the obtained results, the model has a high degree of subjectivity. So, the paper combines data envelopment analysis with fuzzy comprehensive evaluation model, and establishes evaluation model of fuzzy data envelopment analysis. Ultimately, the paper improves the reliable and scientific evaluation model.

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48

Patel, Deepika, Dr Pramod S. Nair, and Mr Rudresh Shah. "Privacy Preserving Sensitive Data Coverage: Design and Analysis." IJIREEICE 5, no. 3 (2017): 147–51. http://dx.doi.org/10.17148/ijireeice.2017.5334.

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49

Brand, David G., Donald G. Leckie, and Edward E. Cloney. "Forest regeneration surveys: Design, data collection, and analysis." Forestry Chronicle 67, no. 6 (1991): 649–57. http://dx.doi.org/10.5558/tfc67649-6.

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Regeneration surveys have always been looked on as a necessary evil in silviculture. Huge amounts of data have been collected, only to answer simple questions or to be filed and never used. This paper addresses the possibility of changing regeneration surveys from simple legislative requirements, into components of the forestry information system. Current technology allows the development of sophisticated decision support systems, and this changes the whole perspective on information needed from regeneration surveys. Depending on the level of information needed, ground surveys or aerial survey

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50

Sui, Shou Gang, Jin Dong Tan, and Shou Yin Lu. "Design Principle and Data Analysis of Clamp Meter." Applied Mechanics and Materials 336-338 (July 2013): 1570–73. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1570.

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Clamp meter is widely used in the production. It is divided into the current clamp meter, voltage clamp meter and resistance clamp meter according to its function. In this article, design idea and design method of a resistance clamp meter are given. Taking the resistance clamp meter as the object, from signal generation, sensing, detection, filtering, and other aspects the design principle and circuit model is described, the actual data measured, and the reason of measurement error analyzed.

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