Journal articles: 'Statistical data science'

1

Shanmugam, Ramalingam. "Statistical data science." Journal of Statistical Computation and Simulation 90, no. 9 (June 12, 2019): 1733. http://dx.doi.org/10.1080/00949655.2019.1628902.

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Cai, Junhui, Avishai Mandelbaum, Chaitra H. Nagaraja, Haipeng Shen, and Linda Zhao. "Statistical Theory Powering Data Science." Statistical Science 34, no. 4 (November 2019): 669–91. http://dx.doi.org/10.1214/19-sts754.

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GRANT, ROBERT. "STATISTICAL LITERACY IN THE DATA SCIENCE WORKPLACE." STATISTICS EDUCATION RESEARCH JOURNAL 16, no. 1 (May 31, 2017): 17–21. http://dx.doi.org/10.52041/serj.v16i1.207.

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Statistical literacy, the ability to understand and make use of statistical information including methods, has particular relevance in the age of data science, when complex analyses are undertaken by teams from diverse backgrounds. Not only is it essential to communicate to the consumers of information but also within the team. Writing from the perspective of a statistician who later taught himself about data visualisation and machine learning, I consider some pitfalls for ommunication and drivers of behaviour within the team. Recruiters and managers also play a part in creating a workplace where speed and novelty are sometimes over-valued. Statisticians have a duty to educate and shape this exciting new workplace. First published May 2017 at Statistics Education Research Journal Archives

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Blei, David M., and Padhraic Smyth. "Science and data science." Proceedings of the National Academy of Sciences 114, no. 33 (August 7, 2017): 8689–92. http://dx.doi.org/10.1073/pnas.1702076114.

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Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. In this article, we ask why scientists should care about data science. To answer, we discuss data science from three perspectives: statistical, computational, and human. Although each of the three is a critical component of data science, we argue that the effective combination of all three components is the essence of what data science is about.

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Morse-Gagne, E. E. "Culturomics: Statistical Traps Muddy the Data." Science 332, no. 6025 (March 31, 2011): 35. http://dx.doi.org/10.1126/science.332.6025.35-b.

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EFRON, B., and R. TIBSHIRANI. "Statistical Data Analysis in the Computer Age." Science 253, no. 5018 (July 12, 1991): 390–95. http://dx.doi.org/10.1126/science.253.5018.390.

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Vansteelandt, Stijn. "Statistical Modelling in the Age of Data Science." Observational Studies 7, no. 1 (2021): 217–28. http://dx.doi.org/10.1353/obs.2021.0013.

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Chen, Yuxin, Yuejie Chi, Jianqing Fan, and Cong Ma. "Spectral Methods for Data Science: A Statistical Perspective." Foundations and Trends® in Machine Learning 14, no. 5 (2021): 566–806. http://dx.doi.org/10.1561/2200000079.

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MacGillivray, Helen. "Data science, statistical investigations, team sport, and assessment." Teaching Statistics 41, no. 1 (January 24, 2019): 1–2. http://dx.doi.org/10.1111/test.12189.

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Reid, Nancy. "Statistical science in the world of big data." Statistics & Probability Letters 136 (May 2018): 42–45. http://dx.doi.org/10.1016/j.spl.2018.02.049.

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Washio, Takashi. "Special Issue on Data-Mining and Statistical Science." New Generation Computing 27, no. 4 (August 2009): 281–84. http://dx.doi.org/10.1007/s00354-009-0065-0.

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GOULD, ROBERT. "DATA LITERACY IS STATISTICAL LITERACY." STATISTICS EDUCATION RESEARCH JOURNAL 16, no. 1 (May 31, 2017): 22–25. http://dx.doi.org/10.52041/serj.v16i1.209.

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Past definitions of statistical literacy should be updated in order to account for the greatly amplified role that data now play in our lives. Experience working with high-school students in an innovative data science curriculum has shown that teaching statistical literacy, augmented by data literacy, can begin early. First published May 2017 at Statistics Education Research Journal Archives

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Stepanova, O. A., G. A. Didenko, and S. T. Kasyuk. "APPLICATION OF THE STATISTICAL PACKAGE PSPP (PROGRAM FOR STATISTICAL ANALYSIS OF SAMPLED DATA) FOR COURSE «MEDICAL COMPUTER SCIENCE»." Современные наукоемкие технологии (Modern High Technologies) 1, no. 6 2021 (2021): 197–202. http://dx.doi.org/10.17513/snt.38722.

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Utts, Jessica. "Enhancing Data Science Ethics Through Statistical Education and Practice." International Statistical Review 89, no. 1 (March 18, 2021): 1–17. http://dx.doi.org/10.1111/insr.12446.

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Demeny, Paul, and Georg P. Muller. "Comparative World Data: A Statistical Handbook for Social Science." Population and Development Review 15, no. 1 (March 1989): 158. http://dx.doi.org/10.2307/1973414.

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Higuchi, Tomoyuki, and Takashi Washio. "Preface: Featured section on data-mining and statistical science." Annals of the Institute of Statistical Mathematics 60, no. 4 (October 24, 2008): 697–98. http://dx.doi.org/10.1007/s10463-008-0208-y.

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Tsuda, Hiroshi. "Establishment of data-driven statistical tourism science and demonstration of its effectiveness." Impact 2021, no. 3 (March 29, 2021): 49–51. http://dx.doi.org/10.21820/23987073.2021.3.49.

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Tourism is an invaluable industry for many countries for many reasons, including the jobs and incomes it provides and the benefits to the economy. Tourism is of particular importance in Japan where the country is working towards a national growth strategy. Measures implemented by the Japanese Government to promote tourism have seen success in this area but the Covid-19 pandemic has presented challenges. Professor Hiroshi Tsuda, Department of Mathematical Sciences, Doshisha University, Japan, is working on research surrounding the establishment of data-driven statistical tourism science and the demonstration of its effectiveness in order to overcome these challenges. The goal of his current work is to help tourist destinations, tourist facilities and lodging facilities to achieve management sustainability, ultimately sustainably revitalising Japan's tourism industry. The innovative methodologies that Tsuda and the team are using in their work are big data analytical methods combined with explainable artificial intelligence (XAI) technology. XAI technology is an AI programmed to explain its decision-making process in a way that can be understood by humans, thereby ensuring the decision-making process is fair. Using XAI technology, the team will develop a tourist support system that can make valuable predictions. The researchers are also facilitating the creation of a new field called 'human and design social science', which could prove beneficial in policy and management science.

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Emmert-Streib, Frank, and Matthias Dehmer. "Defining Data Science by a Data-Driven Quantification of the Community." Machine Learning and Knowledge Extraction 1, no. 1 (December 19, 2018): 235–51. http://dx.doi.org/10.3390/make1010015.

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Data science is a new academic field that has received much attention in recent years. One reason for this is that our increasingly digitalized society generates more and more data in all areas of our lives and science and we are desperately seeking for solutions to deal with this problem. In this paper, we investigate the academic roots of data science. We are using data of scientists and their citations from Google Scholar, who have an interest in data science, to perform a quantitative analysis of the data science community. Furthermore, for decomposing the data science community into its major defining factors corresponding to the most important research fields, we introduce a statistical regression model that is fully automatic and robust with respect to a subsampling of the data. This statistical model allows us to define the ‘importance’ of a field as its predictive abilities. Overall, our method provides an objective answer to the question ‘What is data science?’.

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Glymour, Clark, David Madigan, Daryl Pregibon, and Padhraic Smyth. "Statistical inference and data mining." Communications of the ACM 39, no. 11 (November 1996): 35–41. http://dx.doi.org/10.1145/240455.240466.

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20

Wilson, James H. "Statistical comparison of fatigue data." Journal of Materials Science Letters 7, no. 3 (March 1988): 307–8. http://dx.doi.org/10.1007/bf01730208.

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Agterberg, Frederick P. "Statistical analysis of circular data." Dynamics of Atmospheres and Oceans 21, no. 2-3 (December 1994): 215–18. http://dx.doi.org/10.1016/0377-0265(94)90011-6.

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Pesenson, Misha Z., Santosh K. Suram, and John M. Gregoire. "Statistical Analysis and Interpolation of Compositional Data in Materials Science." ACS Combinatorial Science 17, no. 2 (January 14, 2015): 130–36. http://dx.doi.org/10.1021/co5001458.

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Lai, Tze Leung. "Data Science, Statistical Modeling, and Financial and Health Care Reforms." Notices of the International Congress of Chinese Mathematicians 1, no. 2 (2013): 47–57. http://dx.doi.org/10.4310/iccm.2013.v1.n2.a6.

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24

Freeman, Jim, Vic Barnett, and Toby Lewis. "Outliers in Statistical Data." Journal of the Operational Research Society 46, no. 8 (August 1995): 1034. http://dx.doi.org/10.2307/3009915.

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Kolpak, Valeria, Michael Mogilevsky, Dmitriy Chugunin, Aleksandr Chernyshov, Irina Moiseenko, A. Kumamoto, F. Tsuchiya, et al. "Statistical properties of auroral kilometer radiation: based on ERG (ARASE) satellite data." Solar-Terrestrial Physics 7, no. 1 (March 29, 2021): 11–16. http://dx.doi.org/10.12737/stp-71202102.

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In this work, we have studied the signals of auroral kilometer radiation (AKR) from sources in the auroral regions of the Northern and Southern hemispheres simultaneously recorded by one satellite. We have carried out a detailed statistical analysis of more than 20 months of continuous AKR measurements made by the ERG satellite (also known as Arase). This made it possible to confirm the previously obtained results on the location of AKR sources and seasonal changes in the radiation intensity. Open questions about the processes in the AKR source can be solved using data on the radiation pattern under various geomagnetic conditions. To answer these questions, we have estimated the cone angle of directional diagrams in the dusk and dawn sectors of Earth’s magnetosphere.

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Yu, Bin, and Karl Kumbier. "Veridical data science." Proceedings of the National Academy of Sciences 117, no. 8 (February 13, 2020): 3920–29. http://dx.doi.org/10.1073/pnas.1901326117.

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Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

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Chattopadhyay, S., D. K. Pratihar, and S. C. De Sarkar. "Statistical modeling of psychosis data." Computer Methods and Programs in Biomedicine 100, no. 3 (December 2010): 222–36. http://dx.doi.org/10.1016/j.cmpb.2010.03.017.

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Baek, Young Min, and Misa Park. "Introduction to Statistical Analytic Techniques for Text Data and Suggestions for Social Sciences in the Age of Data Science." Socail Science Review 49, no. 1 (May 31, 2018): 188–210. http://dx.doi.org/10.31502/ssri.49.1.9.

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Chung, Jaewon, Eric Bridgeford, Jesús Arroyo, Benjamin D. Pedigo, Ali Saad-Eldin, Vivek Gopalakrishnan, Liang Xiang, Carey E. Priebe, and Joshua T. Vogelstein. "Statistical Connectomics." Annual Review of Statistics and Its Application 8, no. 1 (March 7, 2021): 463–92. http://dx.doi.org/10.1146/annurev-statistics-042720-023234.

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The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.

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Newman, William I., Martha P. Haynes, and Yervant Terzian. "Redshift data and statistical inference." Astrophysical Journal 431 (August 1994): 147. http://dx.doi.org/10.1086/174474.

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31

Nelson, Larry A. "A Statistical Editor’s Viewpoint of Statistical Usage in Horticultural Science Publications." HortScience 24, no. 1 (February 1989): 53–57. http://dx.doi.org/10.21273/hortsci.24.1.53.

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Abstract Other authors in this series are dealing with the technical aspects of statistical applications, such as experimental design and data analysis. This paper deals more with the reporting phase. But, because this phase is not a phase unto itself, we need to look at all interfaces of statistics and horticultural science research.

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Gariazzo, Claudio, Vincenzo Papaleo, and Armando Pelliccioni. "Statistical comparison of modelled and SODAR measured turbulence data in a coastal area." Meteorologische Zeitschrift 16, no. 4 (August 30, 2007): 383–92. http://dx.doi.org/10.1127/0941-2948/2007/0208.

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33

Emmert-Streib, Frank, and Matthias Dehmer. "Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference." Machine Learning and Knowledge Extraction 1, no. 3 (August 12, 2019): 945–61. http://dx.doi.org/10.3390/make1030054.

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Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlying logic behind statistical hypothesis testing, the formal meaning of its components and their connections. Our presentation is applicable to all statistical hypothesis tests as generic backbone and, hence, useful across all application domains in data science and artificial intelligence.

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ENGEL, JOACHIM. "STATISTICAL LITERACY FOR ACTIVE CITIZENSHIP: A CALL FOR DATA SCIENCE EDUCATION." STATISTICS EDUCATION RESEARCH JOURNAL 16, no. 1 (May 31, 2017): 44–49. http://dx.doi.org/10.52041/serj.v16i1.213.

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Data are abundant, quantitative information about the state of society and the wider world is around us more than ever. Paradoxically, recent trends in the public discourse point towards a post-factual world that seems content to ignore or misrepresent empirical evidence. As statistics educators we are challenged to promote understanding of statistics about society. In order to re-root public debate to be based on facts instead of emotions and to promote evidence-based policy decisions, statistics education needs to embrace two areas widely neglected in secondary and tertiary education: understanding of multivariate phenomena and the thinking with and learning from complex data. First published May 2017 at Statistics Education Research Journal Archives

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35

Gramacy, Robert B. "Book Review: Computer age statistical inference: Algorithms, evidence, and data science." Bulletin of the American Mathematical Society 56, no. 1 (January 25, 2018): 137–42. http://dx.doi.org/10.1090/bull/1611.

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Nguyen, H. Chau, Riccardo Zecchina, and Johannes Berg. "Inverse statistical problems: from the inverse Ising problem to data science." Advances in Physics 66, no. 3 (June 29, 2017): 197–261. http://dx.doi.org/10.1080/00018732.2017.1341604.

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G.W.A.D. "Practical methods for reliability data analysis (Oxford statistical science series - 14)." Microelectronics Reliability 35, no. 8 (August 1995): 1196–97. http://dx.doi.org/10.1016/0026-2714(95)90018-7.

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Machanavajjhala, Ashwin, and Daniel Kifer. "Designing statistical privacy for your data." Communications of the ACM 58, no. 3 (February 23, 2015): 58–67. http://dx.doi.org/10.1145/2660766.

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39

Susko, Alexander Q., and Zachary T. Brym. "An Introduction to R Statistical Computing for Horticultural Science." HortTechnology 26, no. 5 (October 2016): 588–91. http://dx.doi.org/10.21273/horttech03339-16.

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We present the format for a workshop on introductory computer programming, which was held at the 2015 American Society for Horticultural Science (ASHS) Annual Conference in New Orleans, LA. The main workshop objective was to familiarize attendees with basic computer programming, including data structures, data management, and data analysis. The workshop used the general programming language R, though the concepts and principles presented are transferable across programming languages. Given the increased importance of statistical analysis in the agricultural sciences, the workshop was well attended. Participants appreciated the opportunity to improve their computational literacy and supported follow-up workshops like this at future ASHS events. We have released the presentation and the companion R script online.

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Spielman, Seth, Ningchuan Xiao, Samantha Cockings, and Robert Tanton. "Statistical systems and census data in the spatial sciences." Computers, Environment and Urban Systems 63 (May 2017): 1–2. http://dx.doi.org/10.1016/j.compenvurbsys.2017.02.001.

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41

Mihăescu, Constanța, Adrian Oţoiu, Alina Profiroiu, and Ileana Niculescu-Aron. "Investigating students’ use of official statistical data." Proceedings of the International Conference on Applied Statistics 1, no. 1 (October 1, 2019): 329–42. http://dx.doi.org/10.2478/icas-2019-0029.

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Abstract This paper presents the perceptions of social science students about the use of official statistical data, in the context of active learning of Statistics, and other topics related to Applied Statistics. In order to make these courses more attractive, and to challenge and stimulate statistical education, our students work on projects in which they use official statistical data to explore practical, real-life issues. Their attitudes and perceptions regarding official statistical data sources are very important, both for acquisition of statistical analysis skills, essential for their future professional life, and for improvement of the official data sources. Therefore, we conducted a custom-made survey among students from Romanian higher education institutions (HEIs) and gathered a database with 334 responses, which allowed us to identify the main characteristics, problems and solutions concerning the use of statistical official data sources by university students.

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42

Duan, Naihua. "Big Data and Small Data: Reflections on data science, statistical modeling, and financial and health care reforms." Notices of the International Congress of Chinese Mathematicians 2, no. 1 (2014): 22–26. http://dx.doi.org/10.4310/iccm.2014.v2.n1.a5.

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Covell, David G., and Prem K. Narang. "Statistical Analysis of Drug Disposition Data." Clinical Research Practices and Drug Regulatory Affairs 7, no. 4-5 (January 1989): 245–81. http://dx.doi.org/10.3109/10601338909020562.

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44

CADORET, M., S. LÊ, and J. PAGÈS. "STATISTICAL ANALYSIS OF HIERARCHICAL SORTING DATA." Journal of Sensory Studies 26, no. 2 (January 27, 2011): 96–105. http://dx.doi.org/10.1111/j.1745-459x.2010.00326.x.

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Herranz, Javier, Stan Matwin, Jordi Nin, and Vicenç Torra. "Classifying data from protected statistical datasets." Computers & Security 29, no. 8 (November 2010): 875–90. http://dx.doi.org/10.1016/j.cose.2010.05.005.

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46

French, Simon, M. J. Crowder, A. C. Kimber, R. L. Smith, and T. J. Sweeting. "Statistical Analysis of Reliability Data." Journal of the Operational Research Society 43, no. 6 (June 1992): 644. http://dx.doi.org/10.2307/2583024.

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French, Simon. "Statistical Analysis of Reliability Data." Journal of the Operational Research Society 43, no. 6 (June 1992): 644. http://dx.doi.org/10.1057/jors.1992.94.

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48

Dubois, Didier, and Henri Prade. "Fuzzy sets and statistical data." European Journal of Operational Research 25, no. 3 (January 1986): 345–56. http://dx.doi.org/10.1016/0377-2217(86)90266-3.

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Kulkarni, Nishant. "Olympic Data Analysis using Data Science." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 855–61. http://dx.doi.org/10.22214/ijraset.2022.48046.

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Abstract: The Olympic games are international sports events with more than 200 nations participating in various competitions. The Sportspersons from various countries participate in competitions and make their countries proud of their excellence in sports. The primary objective of this paper is to analyze the Olympic dataset using python to compare overall performance of countries and to evaluate the contribution of each country in the Olympics. These analyses will give deeper insight into the performance of countries in Olympics over the years and helps sportspersons to quickly analyze their own and the competitor’s performance. In this paper, the exploratory data analysis techniques are used to provide comparison between performance of various countries and the contribution of each country in the Olympics. Visualization of Olympics dataset in many aspects provides the status of countries in Olympics and helps countries with poor performance to produce quality players and improve nation’s performance in Olympics. Despite a lot of hard work, many countries or players are unable to perform well during the events and grab medals whereas there are many countries that perform very well in the event and secure many medals. An analysis needs to be done by each country to evaluate the previous statistics which will detect the mistakes which they have done previously and will also help them in future development. Visualization of the data over various factors will provide us with the statistical view of the various factors which lead to the evolution of the Olympic Games and Improvement in the performance of various Countries/Players over time. The primary objective of this Research paper is to analyze the large Olympic dataset using Exploratory Data Analysis to evaluate the evolution of the Olympic Games over the years.

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Besse, Philippe C., Herv� Cardot, Robert Faivre, and Michel Goulard. "Statistical modelling of functional data." Applied Stochastic Models in Business and Industry 21, no. 2 (2005): 165–73. http://dx.doi.org/10.1002/asmb.539.

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Journal articles on the topic 'Statistical data science'

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