Academic literature on the topic 'Statistical visualizing'

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Journal articles on the topic "Statistical visualizing"

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de Levie, Robert. "Visualizing Statistical Concepts." Journal of Chemical Education 85, no. 5 (2008): 635. http://dx.doi.org/10.1021/ed085p635.1.

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Allen, Genevera I., Frederick Campbell, and Yue Hu. "Comments on “visualizing statistical models”: Visualizing modern statistical methods for Big Data." Statistical Analysis and Data Mining: The ASA Data Science Journal 8, no. 4 (2015): 226–28. http://dx.doi.org/10.1002/sam.11272.

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Katsaounis, Tena I. "Visualizing Statistical Models and Concepts." Technometrics 45, no. 2 (2003): 175–76. http://dx.doi.org/10.1198/tech.2003.s138.

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Williams, Brian J. "Visualizing Statistical Models and Concepts." Journal of the American Statistical Association 99, no. 465 (2004): 292. http://dx.doi.org/10.1198/jasa.2004.s315.

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Pfahler, Lori B., and William G. Jacoby. "Statistical Graphics for Visualizing Multivariate Data." Technometrics 41, no. 3 (1999): 262. http://dx.doi.org/10.2307/1270570.

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Holmes, Susan, and William G. Jacoby. "Statistical Graphics for Visualizing Multivariate Data." Journal of the American Statistical Association 94, no. 447 (1999): 981. http://dx.doi.org/10.2307/2670014.

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Pfahler, Lori B. "Statistical Graphics for Visualizing Multivariate Data." Technometrics 41, no. 3 (1999): 261–62. http://dx.doi.org/10.1080/00401706.1999.10485674.

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Heckler, Charles E. "Statistical Graphics for Visualizing Multivariate Data." Technometrics 41, no. 4 (1999): 369. http://dx.doi.org/10.1080/00401706.1999.10485938.

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Wickham, Hadley, Dianne Cook, and Heike Hofmann. "Visualizing statistical models: Removing the blindfold." Statistical Analysis and Data Mining: The ASA Data Science Journal 8, no. 4 (2015): 203–25. http://dx.doi.org/10.1002/sam.11271.

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Braşoveanu, Adrian M. P., Marta Sabou, Arno Scharl, Alexander Hubmann-Haidvogel, and Daniel Fischl. "Visualizing statistical linked knowledge for decision support." Semantic Web 8, no. 1 (2016): 113–37. http://dx.doi.org/10.3233/sw-160225.

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Dissertations / Theses on the topic "Statistical visualizing"

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Hazarika, Subhashis. "Statistical and Machine Learning Approaches For Visualizing and Analyzing Large-Scale Simulation Data." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574692702479196.

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Okada, Daigo. "Decomposition of a set of distributions in extended exponential family form for distinguishing multiple oligo-dimensional marker expression profiles of single-cell populations and visualizing their dynamics." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263569.

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Peterson, Nina Marie. "Visualizing incomplete data in multidimensional databases." Online access for everyone, 2006. http://www.dissertations.wsu.edu/Thesis/Spring2006/n%5Fpeterson%5F050106.pdf.

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Sun, Mingxuan. "Visualizing and modeling partial incomplete ranking data." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45793.

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Analyzing ranking data is an essential component in a wide range of important applications including web-search and recommendation systems. Rankings are difficult to visualize or model due to the computational difficulties associated with the large number of items. On the other hand, partial or incomplete rankings induce more difficulties since approaches that adapt well to typical types of rankings cannot apply generally to all types. While analyzing ranking data has a long history in statistics, construction of an efficient framework to analyze incomplete ranking data (with or without ties)
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Abicht, Konrad, Georges Alkhouri, Natanael Arndt, Roy Meissner, and Michael Martin. "CubeViz.js: A Lightweight Framework for Discovering and Visualizing RDF Data Cubes." Gesellschaft für Informatik, 2017. https://ul.qucosa.de/id/qucosa%3A32064.

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In this paper we present CubeViz.js, the successor of CubeViz, as an approach for lightweight visualization and exploration of statistical data using the RDF Data Cube vocabulary. In several use cases, such as the European Unions Open Data Portal, in which we deployed CubeViz, we were able to gather various requirements that eventually led to the decision of reimplementing CubeViz as JavaScript-only application. As part of this paper we showcase major functionalities of CubeViz.js and its improvements in comparison to the prior version.
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Li, Chunyang. "Extracting and Visualizing Data from Mobile and Static Eye Trackers in R and Matlab." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6880.

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Eye tracking is the process of measuring where people are looking at with an eye tracker device. Eye tracking has been used in many scientific fields, such as education, usability research, sports, psychology, and marketing. Eye tracking data are often obtained from a static eye tracker or are manually extracted from a mobile eye tracker. Visualization usually plays an important role in the analysis of eye tracking data. So far, there existed no software package that contains a whole collection of eye tracking data processing and visualization tools. In this dissertation, we review the eye tra
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Baker, Dylan. "The Document Similarity Network: A Novel Technique for Visualizing Relationships in Text Corpora." Scholarship @ Claremont, 2017. https://scholarship.claremont.edu/hmc_theses/100.

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With the abundance of written information available online, it is useful to be able to automatically synthesize and extract meaningful information from text corpora. We present a unique method for visualizing relationships between documents in a text corpus. By using Latent Dirichlet Allocation to extract topics from the corpus, we create a graph whose nodes represent individual documents and whose edge weights indicate the distance between topic distributions in documents. These edge lengths are then scaled using multidimensional scaling techniques, such that more similar documents are cluste
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Books on the topic "Statistical visualizing"

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Farebrother, R. W. Visualizing statistical models and concepts. Marcel Dekker, 2002.

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Jacoby, William. Statistical Graphics for Visualizing Multivariate Data. SAGE Publications, Inc., 1998. http://dx.doi.org/10.4135/9781412985970.

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Jacoby, William G. Statistical graphics for visualizing multivariate data. Sage Publications, 1998.

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Cleveland, William S. Visualizing data. At&T Bell Laboratories, 1993.

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Satdarova, Faina. DIFFRACTION ANALYSIS OF DEFORMED METALS: Theory, Methods, Programs. Academus Publishing, 2019. http://dx.doi.org/10.31519/monography_1598.

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General analysis of the distribution of crystals orientation and dislocation density in the polycrystalline system is presented. 
 Recovered information in diffraction of X-rays adopting is new to structure states of polycrystal. Shear phase transformations in metals — at the macroscopic and microscopic levels — become a clear process. 
 Visualizing the advances is produced by program included in package delivered. Mathematical models developing, experimental design, optimal statistical estimation, simulation the system under study and evolution process on loading serves as instrumen
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Carr, Daniel B. Visualizing data patterns with micromaps. Chapman & Hall/CRC, 2010.

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McKallip, Curtis. Visualizing economics: The art of investing without statistics. Fresh Ink, 1991.

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Farebrother, R. W. Visualizing Statistical Models and Concepts. Taylor & Francis Group, 2002.

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Schyns, Michael, and R. W. Farebrother. Visualizing Statistical Models and Concepts. Taylor & Francis Group, 2020.

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Schyns, Michael, and R. W. Farebrother. Visualizing Statistical Models and Concepts. Taylor & Francis Group, 2002.

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Book chapters on the topic "Statistical visualizing"

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Peng, Roger D., and Duncan Temple Lang. "Caching and Visualizing Statistical Analyses." In Biomedical Informatics for Cancer Research. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-5714-6_17.

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Josselin, Jean-Michel, and Benoît Le Maux. "Measuring and Visualizing Associations." In Statistical Tools for Program Evaluation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52827-4_4.

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Darius, P., J.-P. Ottoy, A. Solomin, O. Thas, B. Raeymaekers, and S. Michiels. "A collection of applets for visualizing statistical concepts." In COMPSTAT. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57678-2_29.

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Di Nuzzo, Cinzia, Donatella Vicari, and Alessia Vitozzi. "A Candecomp/Parafac Approach for Visualizing Flows of Asymmetric Proximity Matrices." In Italian Statistical Society Series on Advances in Statistics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-95995-0_27.

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Ostash, Bohdan, and Maria Anisimova. "Visualizing Codon Usage Within and Across Genomes: Concepts and Tools." In Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2445-5_13.

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Sharma, Madhuri, and Abhilasha Singh. "Unveiling the Statistical Power of MLE in Visualizing EHR Software Reliability Using Weibull Distribution." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3244-2_9.

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Marriott, Kimbal. "Visualizing Statistics." In The Golden Age of Data Visualization. CRC Press, 2024. http://dx.doi.org/10.1201/9781003507642-9.

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Ling, Jin, and Nadezda Sorokina. "Visualizing and Comparing Online Travel Reviews of the Great Walls: A Data Mining Approach." In Information and Communication Technologies in Tourism 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94751-4_39.

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AbstractThis research employs two samples of heritage sites of the Great Wall of China (Ba daling Great Wall and Mu tianyu Great Wall) and their 21000 reviews on TripAdvisor to visualize and induce feature-related comparisons. Word2vec and D3.js are applied for statistical computing and graphing Minimal Spanning Tree (MST) and ThemeRiver. The applications of MST and ThemeRiver are used to delineate outstanding features and clearer feature relationships. In terms of methodology, we applied an innovative research route to combine MST with ThemeRiver to visualize travellers’ online comments. At the same time, the visual results obtained are combined with qualitative analysis to generate valuable, intuitive summaries that can be used for reference in future research. Practically, the results disclose that although both sites are highly enjoyed by tourists, they are significantly different in terms of service, infrastructure and scenery. This article has implications for policymakers and practitioners with regard to making use of online reviews to gather authentic visitor comments on the Great Wall.
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Valtonen, Teppo. "Basic statistics." In Visualizing Surveys in R. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003279815-10.

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Witczak, Marcin, Lothar Seybold, Eric Bulach, and Niko Maucher. "Mastering System Monitoring and Control." In Modern IoT Onboarding Platforms for Advanced Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33623-2_5.

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AbstractThe chapter aims at exploiting the methods and tools described in the preceding parts to develop a set of selected process monitoring and control schemes. In particular, it starts with the transportation system, which operates on a set of selected routes. To measure the performance of such a system, a suitable cost function is introduced, which can be monitored and controlled by the designated supervisor. The proposed approach also allows determining the availability of the transportation system, which can be used as an additional measure for performance improvements. Subsequently, a quality control system design approach is proposed. The proposed system can be either manual or automatic. Irrespective of the selected method, it is shown how to determine a set of suitable statistical measures along with the p chart. Such a quality control system is capable of making binary quality decisions about the product being controlled. Thus, to overcome this restriction, a demerit quality control system is introduced. As a result, a set of useful statistical measures is obtained along with the practical demerit control chart. The last process monitoring strategy aims at calculating and visualizing overall equipment efficiency, which is widely perceived as a key measurement tool for assessing both productivity and efficiency. Finally, the chapter is summarized with a set of training exercises, which can be considered the master level test concerning KIS.ME-oriented skills.
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Conference papers on the topic "Statistical visualizing"

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Sugitani, Miwa, Ryosuke Matsuo, Tomoyoshi Yamazaki, et al. "Extracting and Visualizing Frequent Medical Instruction Patterns with Statistical Insights from Multi-Institutional Electronic Medical Record Data." In 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2025. https://doi.org/10.1109/cbms65348.2025.00111.

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Henderson, Hatold. "Visualizing data with dynamic graphics in excel." In Statistics Education and the Communication of Statistics. International Association for Statistical Education, 2005. http://dx.doi.org/10.52041/srap.05204.

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Data Visualisation tools provide deep insight into the structure of data. Dynamic statistical graphics are now widely available in many statistical packages, and even in Excel. I will give examples of the use of dynamic statistical graphics in statistical practice using Excel. Using such techniques, the internally assessed components of NCEA statistics can be presented by students in ways which are relevant, up-to-date and easy to understand.
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Ribes, Alejandro, Joachim Pouderoux, Anne-Laure Popelin, and Bertrand Iooss. "Visualizing statistical analysis of curve datasets in ParaView." In 2014 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2014. http://dx.doi.org/10.1109/vast.2014.7042523.

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Unger, Andrea, Phillip Muigg, Helmut Doleisch, and Heidrun Schumann. "Visualizing Statistical Properties of Smoothly Brushed Data Subsets." In 2008 12th International Conference Information Visualisation (IV). IEEE, 2008. http://dx.doi.org/10.1109/iv.2008.29.

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Günster, Mirco, Hans-Joachim Mittag, and Benjamin Wallenborn. "Employment of new technical platforms for interactively visualizing statistical methods and statistical data." In Statistics education for Progress: Youth and Official Statistics. International Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.131201.

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For a long time, classical media for learning and teaching, such as textbooks and teacher-centred instruction, have been complemented by e-learning components and virtual campus systems. Smartphones and tablets have impressive functionality, and are becoming ubiquitous. There is an urgent need to create resources that will run on these platforms, so that educators are providing resources that run on the tools that students actually use. This paper illustrates how an extended media mix can broaden the scope of statistics and further education. Two complementary approaches are described. The fir
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Mittag, Hans-Joachim. "Using mobile devices for interactively visualizing basic statistical concepts and official data." In Promoting Understanding of Statistics about Society. International Association for Statistical Education, 2016. http://dx.doi.org/10.52041/srap.16507.

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This paper presents interactive learning objects for statistics education that are tailor-made for smartphones but operate as well on any other technical platform. The objects are self- contained and accessible as free educational resources, joined together to a web app. The introductory part of this paper briefly describes the current state of employment of mobile devices for learning and teaching purposes (m-learning). The ensuing part is dedicated to a new approach for m-learning in statistics education that puts heavy emphasis on interactive visualization. The visualization refers to stati
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Tambouris, Efthimios, Evangelos Kalampokis, and Konstantinos Tarabanis. "Visualizing Linked Open Statistical Data to Support Public Administration." In dg.o '17: 18th Annual International Conference on Digital Government Research. ACM, 2017. http://dx.doi.org/10.1145/3085228.3085304.

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Ridgway, Jim, James Nicholson, Pedro Campos, and Sónia Teixeira. "Tools for visualizing data: a review." In Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2018. http://dx.doi.org/10.52041/srap.17201.

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There has been an explosion in the range of tools available for presenting data, many of which are available to support statistics teaching. These include tools that allow users to ‘drag and drop’ data sets (e.g. RAW), tools designed to display particular data sets (e.g. eXplorer) and software libraries (e.g. D3.js). We report on a review of visualisation tools, where we have described the sorts of visualisations facilitated by each tool, along with features such as ease of use and cost. Data visualisations can give new insights into complex data sets, and can be used directly to reshape teach
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Engel, Joachim, Pedro Campos, James Nicholson, Jim Ridgway, and Sónia Teixeira. "Visualizing multivariate data: graphs that tell stories." In New Skills in the Changing World of Statistics Education. International Association for Statistical Education, 2020. http://dx.doi.org/10.52041/srap.20503.

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Important statistical ideas can be introduced via visualizations without heavy mathematics, hence can become accessible to a broader citizenry. Along a few selected examples, from historical to modern, with technology-based data visualizations, we highlight the potential of data visualizations to enhance students’ capacity to reason with complex data and discuss the role of visualization as a tool to strengthen civic participation in democracy.
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Kolouri, Soheil, Saurav Basu, and Gustavo K. Rohde. "Learning and visualizing statistical relationships between protein distributions from microscopy images." In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014). IEEE, 2014. http://dx.doi.org/10.1109/isbi.2014.6867888.

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Reports on the topic "Statistical visualizing"

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Zhan, Peng. Statistical Analysis and Data Visualization in R. Instats Inc., 2022. http://dx.doi.org/10.61700/dizyg5iq1mqj5469.

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This seminar provides a gentle introduction to R for those looking to use it for applied statistics and visualisation, covering a range of essential topics on basic data manipulation, regression analysis, and visualizing data and results. Examples from multiple fields are used to demonstrate the applications. An official Instats certificate of completion is provided at the conclusion of the seminar.
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Paynter, Robin A., Celia Fiordalisi, Elizabeth Stoeger, et al. A Prospective Comparison of Evidence Synthesis Search Strategies Developed With and Without Text-Mining Tools. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepcmethodsprospectivecomparison.

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Background: In an era of explosive growth in biomedical evidence, improving systematic review (SR) search processes is increasingly critical. Text-mining tools (TMTs) are a potentially powerful resource to improve and streamline search strategy development. Two types of TMTs are especially of interest to searchers: word frequency (useful for identifying most used keyword terms, e.g., PubReminer) and clustering (visualizing common themes, e.g., Carrot2). Objectives: The objectives of this study were to compare the benefits and trade-offs of searches with and without the use of TMTs for evidence
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