Littérature scientifique sur le sujet « Statistical visualisation »
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Articles de revues sur le sujet "Statistical visualisation"
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SUTHERLAND, SINCLAIR, and JIM RIDGWAY. "INTERACTIVE VISUALISATIONS AND STATISTICAL LITERACY." STATISTICS EDUCATION RESEARCH JOURNAL 16, no. 1 (2017): 26–30. http://dx.doi.org/10.52041/serj.v16i1.210.
Texte intégralRésumé :
Statistical literacy involves engagement with the data one encounters. New forms of data and new ways to engage with data – notably via interactive data visualisations – are emerging. Some of the skills required to work effectively with these new visualisation tools are described. We argue that interactive data visualisations will have as profound an effect on statistical literacy as the introduction of statistics packages had on statistics in social science in the 1960s. Current conceptualisations of statistical literacy are too passive, lacking the exploration part in data analysis. Statistical literacy should be conceived of as empowerment to engage effectively with evidence, and educators should seek to move students along a pathway from using interactive data visualisations to building them and interpreting what they see. First published May 2017 at Statistics Education Research Journal Archives
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Cuthbert, Carol E., and Noel J. Pearse. "Strategic Data Pattern Visualisation." Journal of Systemics, Cybernetics and Informatics 20, no. 1 (2022): 122–41. http://dx.doi.org/10.54808/jsci.20.01.122.
Texte intégralRésumé :
Data visualisation reveals patterns and provides insights that lead to actions from management, thereby playing a mediating role in the relationship between the internal resources of a firm and its financial performance. In this chapter, contingent resource-based theory is applied to the analysis of big data, treating its visualisation as a mode of interdisciplinary communication. In service industries in general and the legal industry in particular, big data analytics (BDA) is emerging as a decision-making tool for management to achieve competitive advantage. Traditionally, data scientists have delved into data armed with a hypothesis, but increasingly they explore data to discern patterns that lead to hypotheses that are then tested. These big data analytics tools in the hands of data scientists have the potential to unlock firm value and increase revenue and profits, through pattern identification, analysis, and strategic action. This exploratory mode of working can increase complexity and thereby diminish efficient management decision-making and action. However, data pattern visualisation reduces complexity, as it enables interdisciplinary communication between data scientists and managers through the translation of statistical patterns into visualisations that enable actionable management decisions. When data scientists visualise data patterns for managers, this translates uncertainty into reliable conclusions, resulting in effective management decision-making and action. Informed by contingent resource theory and viewing these primary and secondary resources as independent variables and performance outcomes such as revenue and profitability as dependent variables, a conceptual framework is developed. The contingent resource-based theory highlights capabilities emerging from the interrelationship between primary and secondary resources as being central to competitiveness and profitability. Data decision-making systems are viewed as a primary resource, while complementary resources are (1) their completeness of vision (i.e., strategy and innovation) and (2) their ability to execute (i.e., operational capabilities). Data visualisation is therefore crucial as a resource facilitating actionable decisions by management, which in turn enhances firm performance. The balance between expert agents' self-reliance and central control, the entity's values, task attributes, and risk appetite all moderate the type of data visualisation produced by data scientists.
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Singh, Arpita, Basar Imam Mazhari, and Karan Gupta. "Stock Market Prediction and Visualisation." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4460–67. http://dx.doi.org/10.22214/ijraset.2024.61019.
Texte intégralRésumé :
Abstract: Stock price forecasting is a widely discussed and significant topic in both financial and academic circles. The stock market is inherently unpredictable, lacking clear rules for estimating or predicting share prices. Various methods, including technical analysis, fundamental analysis, time series analysis, and statistical analysis, have been employed to forecast stock prices. However, none of these methods consistently serve as reliable prediction tools. In this paper, we explore the implementation, prediction, and analysis of stock market prices. Artificial Neural Networks and Machine Learning prove effective for forecasting stock prices, returns, and modeling stock behavior. By conducting statistical analyses, we establish relationships between selected factors and share prices, contributing to more accurate predictions. While the stock market remains inherently uncertain, this paper aims to apply data analysis and prediction concepts to forecast stock prices. In the era of global digitization, stock market prediction has undergone significant technological advancements, transforming traditional trading models. As market capitalization continues to rise, stock trading becomes a focal point for financial investors. Analysts and researchers have developed tools and techniques to predict stock price movements, aiding decision-making. Advanced models leverage non-traditional textual data from social platforms for market prediction. Machine learning approaches, including text data analytics and ensemble methods, have significantly improved prediction accuracy. However, analyzing and predicting stock markets remains challenging due to dynamic, erratic, and chaotic data. This study explores machine learning-based approaches for stock market prediction, emphasizing a generic framework. By critically analyzing findings from the last decade (2011–2021) from digital libraries like ACM and Scopus, we provide insights for emerging researchers to delve into this promising area.
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Abdul Shukor, Syaimak, and Mas Al Hafiz Samsir. "VISUALISATION OF URBAN SPRAWL WITHIN THE CITY USING TABLEAU." International Journal of Innovation and Industrial Revolution 5, no. 14 (2023): 146–60. http://dx.doi.org/10.35631/ijirev.514011.
Texte intégralRésumé :
The urban sprawl still happening today is causing an imbalance in development between urban and rural areas. Issues such as non-optimal land use, land use development dispersal, land use conversion to municipal use, and reducing forest and agricultural areas need help in effective governance and enforcement. Furthermore, the condition of the village-in-the-city is increasingly threatened due to the urban sprawl. Currently, the existing visualisations are limited to only categorising the city and village boundaries. It cannot show the boundaries of a village-in-the-city area. The vague boundary categorisation caused by the village boundary area in the city cannot be determined clearly when the existing data provide only the city boundary and the village boundary. This study introduced a structured visualisation of village boundaries in the city using TABLEAU to assist agencies in making decisions in formulating development plans, especially for rural areas in the city. Focus Group Discussions (FGD) among the stakeholders are then used to evaluate the effectiveness of the developed visualisation. The visualisation will be based on dashboard development using Tableau software, including statistical information and urban, rural and village-in-the-city boundary information. The existing raw data in shapefiles, excel, and comma-separated values (CSV) formats are processed and integrated before the dashboard can be developed. This paper presented a pilot study for only two states: Negeri Sembilan and Melaka. The results of this study have been able to prove the existence of village-in-the-city visually, which previously could not be confirmed in the form of graphic maps. In addition, the results from the FGD can be concluded that the developed visualisations are promising and could provide the necessary information. Besides helping the agency management make better and more accurate decisions, this study will also benefit the public living within the city's village boundary area. On the other hand, from a scientific point of view, this study can be further explored to visualise the urban sprawl and apply predictive analysis to determine the needs of educational and health facilities in the area.
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Andruszkiewicz, Anna, and Jolanta Korycka-Skorupa. "The cartographic visualisation of spatial data. An example of nature protection areas in the Podlaskie Voivodship (north-eastern Poland)." Polish Cartographical Review 52, no. 3 (2020): 108–23. http://dx.doi.org/10.2478/pcr-2020-0009.
Texte intégralRésumé :
Abstract The authors attempt to visualise nature conservation issues in the Podlaskie Voivodship using a series of small-scale thematic maps. Publicly available spatial and statistical data was used. The authors discuss the effects of applying the adopted methodological solutions. Some of them present a new approach to cartographic visualisation of spatial data. It may be an opportunity to look at various visualisation methods, their effectiveness, and the possibility of visualising nature issues on small-scale thematic maps.
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Breddels, M. A. "Interactive (statistical) visualisation and exploration of a billion objects with vaex." Proceedings of the International Astronomical Union 12, S325 (2016): 299–304. http://dx.doi.org/10.1017/s1743921316012795.
Texte intégralRésumé :
AbstractWith new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new methods of handling and visualizing these data volumes are needed. We show that by calculating statistics on a regular (N-dimensional) grid, visualizations of a billion objects can be done within a second on a modern desktop computer. This is achieved using memory mapping of hdf5 files together with a simple binning algorithm, which are part of a Python library called vaex. This enables efficient exploration or large datasets interactively, making science exploration of large catalogues feasible. Vaex is a Python library and an application, which allows for interactive exploration and visualization. The motivation for developing vaex is the catalogue of the Gaia satellite, however, vaex can also be used on SPH or N-body simulations, any other (future) catalogues such as SDSS, Pan-STARRS, LSST, etc. or other tabular data. The homepage for vaex is http://vaex.astro.rug.nl.
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Lodha, Suresh, Prabath Gunawardane, Erin Middleton, and Ben Crow. "Understanding relationships between global health indicators via visualisation and statistical analysis." Journal of International Development 21, no. 8 (2009): 1152–66. http://dx.doi.org/10.1002/jid.1652.
Texte intégral
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Britten, Anna. "Data viz whiz kids." Significance 20, no. 6 (2023): 42–44. http://dx.doi.org/10.1093/jrssig/qmad099.
Texte intégralRésumé :
Abstract Taylor Swift albums, basketball stars and homework all inspired high-schoolers to show off their data visualisation skills for a colourful American Statistical Association contest this summer
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FORBES, SHARLEEN, JEANETTE CHAPMAN, JOHN HARRAWAY, DOUG STIRLING, and CHRIS WILD. "USE OF DATA VISUALISATION IN THE TEACHING OF STATISTICS: A NEW ZEALAND PERSPECTIVE." STATISTICS EDUCATION RESEARCH JOURNAL 13, no. 2 (2014): 187–201. http://dx.doi.org/10.52041/serj.v13i2.290.
Texte intégralRésumé :
For many years, students have been taught to visualise data by drawing graphs. Recently, there has been a growing trend to teach statistics, particularly statistical concepts, using interactive and dynamic visualisation tools. Free down-loadable teaching and simulation software designed specifically for schools, and more general data visualisation tools are increasingly being used in New Zealand classrooms. This paper discusses four examples: the use of GenStat for Teaching and Learning Schools and Undergraduate (GTL); Auckland University’s iNZight and VIT (Visual Inference Tools) for teaching bootstrapping and randomisation; the CAST e-books, and the use of data visualisation tools to assist learning concepts in official statistics. All these tools are publically available and several are already being used internationally. First published November 2014 at Statistics Education Research Journal Archives
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Sweatman, Winston LeMay, James McGree, Corrie Jacobien Carstens, et al. "Visualisation and statistical modelling techniques for the management of inventory stock levels." ANZIAM Journal 57 (August 28, 2016): 130. http://dx.doi.org/10.21914/anziamj.v57i0.10225.
Texte intégralThèses sur le sujet "Statistical visualisation"
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Albazzaz, Hamza. "Multivariate statistical batch process control and data visualisation based on independent component analysis." Thesis, University of Leeds, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432293.
Texte intégral
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TORTI, FRANCESCA. "Advances in the forward search: methodological and applied contributions." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7791.
Texte intégralRésumé :
The thesis is about the Forward Search, an approach to robust data analysis proposed and developed in the last 15 years mainly by Atkinson, Riani and Cerioli.
It is a general method for detecting unidentified subsets and masked outliers in complex data and for determining their effect on models fitted to the data.
This thesis approaches the Forward Search in the regression context under different perspectives, motivated by issues encountered in concrete application contexts related to the analysis of international trade data.
The main contributions of the thesis can be summarised as follows.
Firstly, it is investigated how the Forward Search achieves its nominal size and how it faces with the multiple testing issue.
Secondly, the Forward Search algorithm is relaxed in order to identify outliers with arbitrary significance levels, other than the standard 1% that is inherent to the method.
Thirdly, it reports the results of a rigorous and extensive assessment of the actual size and power of the Forward Search in comparison with the today reference methods in robust regression (LMS and LTS). The results empirically prove that the Forward Search can achieve at the same time high power and small size.
Fourthly, the Forward Search is extended to identify and validate homogeneous sub-populations in the data that, in regression, manifest as mixtures of linear components.
Finally the Forward Search is addressed from the exploratory data analysis perspective, discussing new dynamic and interactive graphical tools aimed at extracting information from the numerous plots produced by the Forward Search.
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Kobakian, Stephanie Rose. "New algorithms for effectively visualising Australian spatio-temporal disease data." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/203908/1/Stephanie_Kobakian_Thesis.pdf.
Texte intégralRésumé :
This thesis contributes to improvements in effectively communicating population related cancer distributions and the associated burden of cancer on Australian communities. This thesis presents a new algorithm for creating an alternative map displays of tessellating hexagons. Alternative map displays can emphasise statistics in countries that contain densely populated cities. It is accompanied by a software implementation that automates the choice of one hexagon to represent each geographic unit, ensuring the statistic for each is equitably presented. The case study comparing a traditional choropleth map to the alternative hexagon tile map contributes to a growing field of visual inference studies.
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Alfadda, Dalal Abdulaziz. "How Does a ‘Model of Graphics’ Approach and Peer Tutoring Lead to Deep Understanding of Data Visualisation?" Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27203.
Texte intégralRésumé :
This thesis describes how students with no knowledge of descriptive statistics and no experience with data visualisation software learned to produce such visualisations. It proposes a model of graphics approach that enables students to compose a description of a graph and move beyond standard chart types.
Two quasi-experimental studies using a one-group pretest–posttest design were conducted with 62 third-year education students from two Australian universities. Intervention involved learning from exposition through an online module, including instructional videos, worked-out examples, problem-solving, and peer feedback. Participants learned to construct two chart types using the statistical software ggplot2. Participants’ utterances and screens were recorded and subjected to detailed qualitative analysis.
The results suggest that the model of graphics and peer tutoring are effective learning methods for data visualisation production. Analysis of pretest to posttest gains showed significant increases in students’ scores, and the students demonstrated knowledge transfer.
Qualitative analysis showed that during construction of the model of graphics, students engaged in active and constructive learning modes in terms of the Interactive–Constructive–Active–Passive (ICAP) theory. The findings suggest that having the tutor at a higher scoring level than the tutee is most beneficial.
This research contributes to data science and statistics education. It is the first study to examine novices’ initial hours of learning about a theory of statistical graphs and their first use of ggplot2. It lays the ground for the model of graphics competencies and establishes a qualitative framework for analysing students’ learning and interaction. The findings contribute to the field of peer tutoring, particularly reciprocal peer tutoring. The impact of role sequence in reciprocal tutoring has been almost absent from the literature; this study emphasises the potential impact of first role (tutor or tutee) on learning and transfer.
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Laurent, Anabelle. "The analysis of data from on-farm research network : Statistical approaches to test the efficacy of management practices and data visualization." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASB022.
Texte intégralRésumé :
L’expérimentation en réseau d’agriculteurs permet de conduire des essais agronomiques dans des conditions réelles de production. L’intérêt est grandissant car cela représente une opportunité pour mettre en place des essais testant de nouveaux produits et de nouvelles pratiques agricoles dans les champs des agriculteurs. Généralement, les résultats sont présentés sous la forme de rapports individuels (i.e., un rapport résumant les résultats d’un essai en parcelle agricole), mais les informations fournies sont limitées, difficile à généraliser, et cela ne permet pas de synthétiser les résultats recueillis dans les différents essais. De plus, la variabilité de la réponse de rendement est inexplorée alors qu’elle constitue un potentiel pour améliorer la prise de décision par les agriculteurs. L’objectif général de cette thèse est de démontrer l’importance d’identifier des méthodes statistiques appropriées pour analyser et visualiser les données issues d’expérimentation en réseaux d’agriculteurs. Plus précisément, je me suis consacrée à l’analyse des réseaux gérés par Iowa Soybean Association, et d’un cas d’étude français. Un cadre d’analyse des données a été développé pour analyser simultanément plusieurs essais agricoles ayant un protocole expérimental commun et identifier les conditions dans lesquelles un traitement peut ou non être efficace. Ce cadre d’analyse utilise un modèle statistique à effets aléatoires par une approche bayésienne et fournit des estimations de réponse de rendement à l’échelle du réseau et des essais. Ce cadre d’analyse a été mis en application pour 51 pratiques agricoles sur le maïs et le soja au sein d’une application web. Celle-ci comprend des fonctions de visualisation dynamique et interactive des données afin d’améliorer le partage d’informations, et est accessible à un large public pour faciliter l’accès aux résultats issus des réseaux d’expérimentations. Un modèle statistique à effets aléatoires a été utilisé pour calculer des intervalles de prédiction décrivant la gamme des réponses de rendement plausibles pour un nouvel essai (en dehors de l’échantillon testé), et calculer la probabilité que la pratique agricole testée soit inefficace dans une nouvelle parcelle agricole. Selon le niveau de variabilité inter-essais, les intervalles de prédiction étaient 2,2 à 12,1 fois plus larges que les intervalles de confiance des réponses de rendement moyen (c'est-à-dire à l’échelle du réseau) pour toutes les pratiques agricoles testées. L'utilisation des intervalles de prévision et de la probabilité qu’un traitement soit inefficace empêcheront les agriculteurs d’avoir des attentes trop optimistes concernant le gain de rendement dans le cas où celui-ci est significatif à l’échelle du réseau. Le cadre d'analyse des données a été adapté à un réseau d’expérimentation français axé sur l'efficacité des produits de biocontrôle contre le Botrytis cinerea, le bicarbonate de potassium et Aureobasidium pullulans, sur la vigne en production biologique. Les résultats sont en faveur du bicarbonate de potassium car son efficacité sur l’incidence à l’échelle du réseau est plus élevée qu’Aureobasidium pullulans, pour une intensité de la maladie variant de 0 à 10%. Pour ces deux produits de biocontrôle, l'efficacité sur l'incidence pour un nouvel essai est très incertaine pour les niveaux d'intensité de maladie supérieurs à 15 %. Enfin, ce travail de thèse a permis d'étudier l'impact de l'échelle de la parcelle expérimentale (c'est-à-dire à essais en station expérimentale et expérimentation au champ) sur l'effet des pratiques agricoles sur le rendement et d'identifier la cause des écarts potentiels afin de mieux informer la prise de décision au niveau de l'exploitation et d'adapter l'extrapolation des résultats. Ce travail de thèse représente le premier effort majeur de consolidation des résultats d’expérimentation en réseaux d’agriculteurs et fournit des connaissances pour aider à la décision en termes de pratique agricole<br>An on-farm research network is an organization of farmers that conducts agronomic experiments under local conditions. There is growing interest in on-farm research networks because they provide the infrastructure needed to test new products and management practices in farmers’ fields. Often, the results are usually presented as individual reports (i.e., a report summarizing the outcome for one trial), but this provides limited information difficult to generalize and does not allow presenting, in a synthetic way, all the results collected from the different trials. Moreover, there is an unexplored potential in detecting yield response variability patterns for better decision making. The overall objective of this thesis is to demonstrate the importance of identifying appropriate statistical methods for analyzing and visualizing on-farm research network data. Specifically, I focused on analyzing the on-farm research networks managed by the Iowa Soybean Association, and an adaptation was made with a French case-study. A data-analytics framework was developed to analyze multiple trials that use a common protocol and identify the conditions where an imposed treatment may or may not be effective. This framework used a random-effect model through a Bayesian approach and returned yield response estimates at the network and trial levels. The framework was implemented through a web-application for 51 different management practices on corn and soybean. The web-application includes dynamic data visualization features to enhance communication and information sharing, and is accessible to a broad audience to improve accessibility to on-farm research insights. A random-effects statistical model was used to compute prediction intervals describing a range of plausible yield response for a new (out-of-sample) trial, and compute the probability that the tested management practice will be ineffective in a new field. Depending on the level of between-trial variability, the prediction intervals were 2.2–12.1 times larger than confidence intervals for the estimated mean yield responses (i.e., at the network level) for all tested management practices. Using prediction intervals and the probability of ineffective treatment will prevent farmers from over-optimistic expectations that a significant effect at the network level will lead with high certainty to a yield gain on their farms. The data-analytic framework was adapted to a French on-farm research network focusing on the efficacy of biocontrol agent products against Botrytis cinerea, potassium bicarbonate and Aureobasidium pullulans, on organic vine. The results favored potassium bicarbonate as its efficacy on incidence at the network level is higher for diseased intensities between 0% and 10% than for Aureobasidium pullulans. For both biocontrol agents, the efficacy on incidence for a new trial is highly uncertain for intensity levels higher than 15%. Finally, this research investigated the impact of experimental plot scale (i.e., small-plot scale and field scale) on the effect of management practice on crop yield and identified the cause of potential discrepancies to inform on-farm decision-making better and adapt the extrapolation of the results. Taken together, this research represents the first major effort in consolidating results from on-farm research network and provides insight to make better farming management decisions
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Phaweni, Thembani. "Classification and visualisation of text documents using networks." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29534.
Texte intégralRésumé :
In both the areas of text classification and text visualisation graph/network theoretic methods can be applied effectively. For text classification we assessed the effectiveness of graph/network summary statistics to develop weighting schemes and features to improve test accuracy. For text visualisation we developed a framework using established visual cues from the graph visualisation literature to communicate information intuitively. The final output of the visualisation component of the dissertation was a tool that would allow members of the public to produce a visualisation from a text document. We represented a text document as a graph/network. The words were nodes and the edges were created when a pair of words appeared within a pre-specified distance (window) of words from each other. The text document model is a matrix representation of a document collection such that it can be integrated into a machine or statistical learning algorithm. The entries of this matrix can be weighting according to various schemes. We used the graph/network representation of a text document to create features and weighting schemes that could be applied to the text document model. This approach was not well developed for text classification therefore we applied different edge weighting methods, window sizes, weighting schemes and features. We also applied three machine learning algorithms, naïve Bayes, neural networks and support vector machines. We compared our various graph/network approaches to the traditional document model with term frequency inverse-document-frequency. We were interested in establishing whether or not the use of graph weighting schemes and graph features could increase test accuracy for text classification tasks. As far as we can tell from the literature, this is the first attempt to use graph features to weight bag-of-words features for text classification. These methods had been applied to information retrieval (Blanco & Lioma, 2012). It seemed they could also be applied to text classification. The text visualisation field seemed divorced from the text summarisation and information retrieval fields, in that text co-occurrence relationships were not treated with equal importance. Developments in the graph/network visualisation literature could be taken advantage of for the purposes of text visualisation. We created a framework for text visualisation using the graph/network representation of a text document. We used force directed algorithms to visualise the document. We used established visual cues like, colour, size and proximity in space to convey information through the visualisation. We also applied clustering and part-of-speech tagging to allow for filtering and isolating of specific information within the visualised document. We demonstrated this framework with four example texts. We found that total degree, a graph weighting scheme, outperformed term frequency on average. The effect of graph features depended heavily on the machine learning method used: for the problems we considered graph features increased accuracy for SVM classifiers, had little effect for neural networks and decreased accuracy for naïve Bayes classifiers Therefore the impact on test accuracy of adding graph features to the document model is dependent on the machine learning algorithm used. The visualisation of text graphs is able to convey meaningful information regarding the text at a glance through established visual cues. Related words are close together in visual space and often connected by thick edges. Large nodes often represent important words. Modularity clustering is able to extract thematically consistent clusters from text graphs. This allows for the clusters to be isolated and investigated individually to understand specific themes within a document. The use of part-of-speech tagging is effective in both reducing the number of words being displayed but also increasing the relevance of words being displayed. This was made clear through the use of part-of-speech tags applied to the Internal Resistance of Apartheid Wikipedia webpage. The webpage was reduced to its proper nouns which contained much of the important information in the text. Training accuracy is important in text classification which is a task that can often be performed on vast amounts of documents. Much of the research in text classification is aimed at increasing classification accuracy either through feature engineering, or optimising machine learning methods. The finding that total degree outperformed term frequency on average provides an alternative avenue for achieving higher test accuracy. The finding that the addition of graph features can increase test accuracy when matched with the right machine learning algorithm suggests some new research should be conducted regarding the role that graph features can have in text classification. Text visualisation is used as an exploratory tool and as a means of quickly and easily conveying text information. The framework we developed is able to create automated text visualisations that intuitively convey information for short and long text documents. This can greatly reduce the amount of time it takes to assess the content of a document which can increase general access to information.
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Byström, Gustaf. "User centred prototyping iPhone visualisation of XMS Penvision statistics." Thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94736.
Texte intégralRésumé :
XMS Penvision provides a platform to its customers for the creation of paper forms with the special technique this demands and management of their digital pens. The platform spans multiple operating systems but has not yet taken the step over to the iPhone. The ambition of XMS Penvision is to implement this and want to begin by showing statistical administrative information regarding their platform. The purpose of this thesis is to highlight the needs and wishes of XMS Penvision customers with the help of user‐centered methods and to create a prototype of an administrative interface for the iPhone. Usability goals, prototyping, heuristic evaluation and user tests are some of the methods applied during the work and the report describes these methods in theory but also through a discussion around their execution. The end result is a prototype implemented on the iPhone. The prototype uses data taken directly from XMS Penvision systems and users' views are documented.<br>XMS Penvision tillhandahåller idag en plattform till sina kunder för skapande av pappersformulär med den speciella teknik som detta kräver och hanteringen av tillhörande digitala pennor. Plattformen sträcker sig över flera olika operativsystem men har ännu inte tagit steget över till iPhone. XMS Penvisions ambition är att genomföra detta och vill då börja med att visa statistisk administrativ information gällande deras plattform. Syftet med detta examensarbete är att lyfta fram behov och önskemål från XMS Penvisions kunder med hjälp av användarcentrerade metoder och skapa en prototyp över ett administrativt gränssnitt för iPhone. Användbarhetsmål, prototyping, heuristisk utvärdering och användningstest är några av de metoder som under arbetet tillämpades och rapporten beskriver dessa metoder, dels genom teori men också genom diskussion kring tillämpandet. Slutresultatet är en prototyp implementerad på iPhone. Prototypen använder sig av data direkt hämtad från XMS Penvsions system och användarnas åsikter finns dokumenterade.
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Burke, Tommy. "Evaluation of visualisations of geographically weighted regression, with perceptual stability." Thesis, University of St Andrews, 2016. http://hdl.handle.net/10023/15680.
Texte intégralRésumé :
Given the large volume of data that is regularly accumulated, the need to properly manage, efficiently display and correctly interpret, becomes more important. Complex analysis of data is best performed using statistical models and in particular those with a geographical element are best analysed using Spatial Statistical Methods, including local regression. Spatial Statistical Methods are employed in a wide range of disciplines to analyse and interpret data where it is necessary to detect significant spatial patterns or relationships. The topic of the research presented in this thesis is an exploration of the most effective methods of visualising results. A human being is capable of processing a vast amount of data as long as it is effectively displayed. However, the perceptual load will at some point exceed the cognitive processing ability and therefore the ability to comprehend data. Although increases in data scale did increase the cognitive load and reduce processing, prior knowledge of geographical information systems did not result in an overall processing advantage. The empirical work in the thesis is divided into two parts. The first part aims to gain insight into visualisations which would be effective for interpretation and analysis of Geographically Weighted Regression (GWR), a popular Spatial Statistical Method. Three different visualisation techniques; two dimensional, three dimensional and interactive, are evaluated through an experiment comprising two data set sizes. Interactive visualisations perform best overall, despite the apparent lack of researcher familiarity. The increase in data volume can present additional complexity for researchers. Although the evaluation of the first experiment augments understanding of effective visualisation display, the scale at which data can be adequately presented within these visualisations is unclear. Therefore, the second empirical investigation seeks to provide insight into data scalability, and human cognitive limitations associated with data comprehension. The general discussion concludes that there is a need to better inform researchers of the potential of interactive visualisations. People do need to be properly trained to use these systems, but the limits of human perceptual processing also need to be considered in order to permit more efficient and insightful analysis.
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Good, Norman Markus. "Methods for estimating the component biomass of a single tree and a stand of trees using variable probability sampling techniques." Thesis, Queensland University of Technology, 2001. https://eprints.qut.edu.au/37097/1/37097_Good_2001.pdf.
Texte intégralRésumé :
This thesis developed multistage sampling methods for estimating the aggregate biomass of selected tree components, such as leaves, branches, trunk and total, in woodlands in central and western Queensland. To estimate the component biomass of a single tree randomised branch sampling (RBS) and importance sampling (IS) were trialed. RBS and IS were found to reduce the amount of time
and effort to sample tree components in comparison with other standard destructive sampling methods such as ratio sampling, especially when sampling small components such as leaves and small twigs. However, RBS did not estimate leaf and small twig biomass to an acceptable
degree of precision using current methods for creating path selection probabilities. In addition to providing an unbiased estimate of tree component biomass, individual estimates were used for developing allometric regression equations. Equations based on large components
such as total biomass produced narrower confidence intervals than equations developed using
ratio sampling. However, RBS does not estimate small component biomass such as leaves and small wood components with an acceptable degree of precision, and should be mainly used in conjunction with IS for estimating larger component biomass. A whole tree was completely enumerated to set up a sampling space with which RBS could be evaluated under a number of scenarios. To achieve a desired precision, RBS sample size and
branch diameter exponents were varied, and the RBS method was simulated using both analytical and re-sampling methods. It was found that there is a significant amount of natural variation present when relating the biomass of small components to branch diameter, for example. This finding validates earlier decisions to question the efficacy of RBS for estimating small component biomass in eucalypt species. In addition, significant improvements can be made to increase the precision of RBS by increasing the number of samples taken, but more
importantly by varying the exponent used for constructing selection probabilities. To further evaluate RBS on trees with differing growth forms from that enumerated, virtual
trees were generated. These virtual trees were created using L-systems algebra. Decision rules for creating trees were based on easily measurable characteristics that influence a tree's growth and form. These characteristics included; child-to-child and children-to-parent branch diameter relationships, branch length and branch taper. They were modelled using probability
distributions of best fit. By varying the size of a tree and/or the variation in the model describing
tree characteristics; it was possible to simulate the natural variation between trees of similar size
and fonn. By creating visualisations of these trees, it is possible to determine using visual means whether RBS could be effectively applied to particular trees or tree species. Simulation also aided in identifying which characteristics most influenced the precision of RBS, namely, branch length and branch taper. After evaluation of RBS/IS for estimating the component biomass of a single tree, methods for estimating the component biomass of a stand of trees (or plot) were developed and evaluated. A sampling scheme was developed which incorporated both model-based and design-based
biomass estimation methods. This scheme clearly illustrated the strong and weak points
associated with both approaches for estimating plot biomass. Using ratio sampling was more efficient than using RBS/IS in the field, especially for larger tree
components. Probability proportional to size sampling (PPS) -size being the trunk diameter at breast height - generated estimates of component plot biomass that were comparable to those generated using model-based approaches. The research did, however, indicate that PPS is more precise than the use of regression prediction ( allometric) equations for estimating larger components such as trunk or total biomass, and the precision increases in areas of greater biomass. Using more reliable auxiliary information for identifying suitable strata would reduce the amount of within plot variation, thereby increasing precision. PPS had the added advantage
of being unbiased and unhindered by numerous assumptions applicable to the population of interest, the case with a model-based approach. The application of allometric equations in predicting the component biomass of tree species other than that for which the allometric was developed is problematic. Differences in wood density need to be taken into account as well as differences in growth form and within species variability, as outlined in virtual tree simulations. However, the development and application of allometric prediction equations in local species-specific contexts is more desirable than PPS.
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Krusche, Stefan. "Visualisierung und Analyse multivariater Daten in der gartenbaulichen Beratung -Methodik, Einsatz und Vergleich datenanalytischer Verfahren." Doctoral thesis, Humboldt-Universität zu Berlin, Landwirtschaftlich-Gärtnerische Fakultät, 1999. http://dx.doi.org/10.18452/14463.
Texte intégralRésumé :
Ausgangspunkt der vorliegenden Arbeit ist die Suche der gartenbaulichen Beratung nach Visualisierungsmöglichkeiten umfangreicher gartenbaulicher Datensätze, die einerseits zu einer graphischen Zusammenfassung der in den Daten enthaltenen Informationen dienen und die andererseits auf interaktivem Weg Möglichkeiten der graphischen Analyse von Erhebungsdaten liefern. Die weitgehende Freiheit von Modellannahmen, der überwiegend deskriptive Charakter der Untersuchungen, das interaktive, schrittweise Vorgehen in der Auswertung, und die Betonung graphischer Elemente kennzeichnet die Arbeit als Beitrag zur explorativen Datenanalyse. Das ausgewählte Methodenspektrum, das ausführlich besprochen wird, schließt Verfahren der Dimensionserniedrigung (Hauptkomponentenanalyse, Korrespondenzanalyse und mehrdimensionale Skalierung) und darauf aufbauende Biplots, die Analyse gruppierter Daten (Prokrustes-Rotation und Gruppenanalysemodelle in der Hauptkomponentenanalyse), Linienverbände (Liniendiagramme der formalen Begriffsanalyse, Baumdiagramme und graphische Modelle), sowie ergänzende graphische Verfahren, wie zum Beispiel Trellis-Displays, ein. Beispielhaft werden eine betriebsbegleitende Untersuchung mit Cyclamen aus der Beratungspraxis der Landwirtschaftskammer Westfalen-Lippe und die Kennzahlen der Jahre 1992 bis 1994 der Topfpflanzenbetriebe des Arbeitskreises für Betriebswirtschaft im Gartenbau aus Hannover analysiert. Neben einer Vielzahl informativer Einzelergebnisse, zeigt die Arbeit auch auf, daß die qualitativ relativ schlechten Datengrundlagen nur selten eindeutige Schlußfolgerungen zulassen. Sie sensibilisiert also in diesem Bereich für die Problematik, die der explorativen Analyse wenig perfekter Daten innewohnt. Als besonders sinnvolle Hilfsmittel in der graphischen Analyse erweisen sich Biplots, hierarchische Liniendiagramme und Trellis-Displays. Die Segmentierung einer Vielzahl von Objekten in einzelne Gruppen wird durch Klassifikations- und Regressionsbäume vor allem unter dem Gesichtspunkt der Visualisierung gut gelöst, da den entstehenden Baumstrukturen auch die die Segmente bestimmenden Variablen visuell entnommen werden können. Diskrete graphische Modelle bieten schließlich einen guten Ansatzpunkt zur Analyse von multivariaten Beziehungszusammenhängen. Einzelne, nicht in der statistischen Standardsoftware vorhandene Prozeduren sind in eigens erstellten Programmcodes zusammengefaßt und können mit dem Programm Genstat genutzt werden.<br>In order to interpret large data sets in the context of consultancy and extension in horticulture, this thesis attempts to find ways to visually explore horticultural multivariate data, in order to obtain a concise description and summary of the information available in the data and moreover develop possibilities to interactively analyse survey data. The thesis is part of an exploratory data analysis which analyses data without making specific model assumptions, is predominantly descriptive, analyses data step by step in a highly interactive setting, and makes full use of all kinds of graphical displays. The methods used comprise various dimensionality reduction techniques (principal components analysis, correspondence analysis, multidimensional scaling), biplots, the multivariate analysis of grouped data (procrustes rotation and groupwise principal components), graphical models, CART, and line diagrams of formal concept analysis. In addition, further graphical methods are used, like e.g. trellis displays. Data from an on-site investigation of the production process of Cyclamen in 20 nurseries and from the microeconomics indicators of 297 growers in Germany (so called Kennzahlen) from the years 1992 to 1994 are used to demonstrate the analytical capabilities of the methods used. The data present a perfect example of unperfect data, and therefore represent the majority of the data sets that horticultural consultancy has to work with. Thus, it becomes clear, that despite the variety of results, which helps to enhance the understanding of the data at hand, not only the complexity of the processes observed, but also the low data quality make it fairly difficult to arrive at clear cut conclusions. The most helpful tools in the graphical data analysis are biplots, hierarchical line diagrams and trellis displays. Finding an empirical grouping of objects is best solved by classification and regression trees, which provide both, the data segmentation, and an intuitively appealing visualisation and explanation of the derived groups. In order to understand multivariate relationships better, discrete graphical models are well suited. The procedures to carry out a number of the methods which cannot be found in general statistics packages are provided in the form of Genstat codes.
Livres sur le sujet "Statistical visualisation"
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author, Zuur Alain F., ed. A beginner's guide to data exploration and visualisation with R. Highland Statistics Ltd., 2015.
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Mohrenweiser, Jens. Learn Data Visualisations of Descriptive Statistics in Excel for the Gender Pay Report. SAGE Publications Ltd, 2023. http://dx.doi.org/10.4135/9781529668155.
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Farebrother, R. W. Visualizing statistical models and concepts. Marcel Dekker, 2002.
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O'Donoghue, Peter, Ambikesh Jayal, Allistair McRobert, and Giles Oatley. Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance. Taylor & Francis Group, 2018.
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O'Donoghue, Peter, Ambikesh Jayal, Allistair McRobert, and Giles Oatley. Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance. Taylor & Francis Group, 2018.
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O'Donoghue, Peter, Ambikesh Jayal, Allistair McRobert, and Giles Oatley. Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance. Taylor & Francis Group, 2018.
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Data Analytics for business Edition. Data Analytics : for Statisticians Biologists Scientific Research Surveys : Collect Data with Statistical Tables to Fill for Data /analysis *Average Variance Standard Deviation*: Data Visualisation and Statistical Inference Time Series Data Analysis Tracker. Independently Published, 2020.
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Data Analytics for Business : Collect Data Tool with Statistical Tables to Fill for Data Analytics / Analysis *Average Variance Standard Deviation*: Data Visualisation and Statistical Inference Time Series Data Analysis Tracker a Manual for Polls Surveys. Independently Published, 2020.
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Data Analytics for Business : Collect Data Tool with Statistical Tables to Fill for Data Analytics / Analysis *Average Variance Standard Deviation*: Data Visualisation and Statistical Inference Time Series Data Analysis Tracker a Manual for Polls Surveys. Independently Published, 2020.
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Data Analytics for Business : Collect Data Tool with Statistical Tables to Fill for Data Analytics / Analysis *Average Variance Standard Deviation*: Data Visualisation and Statistical Inference, Time Series Data Analysis Tracker, a Manual for Polls Surveys. Independently Published, 2020.
Trouver le texte intégralChapitres de livres sur le sujet "Statistical visualisation"
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Aitkin, Murray. "Data visualisation." In Introduction to Statistical Modelling and Inference. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003216025-9.
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Shardt, Yuri A. W. "Data Visualisation." In Using Excel to Solve Statistical Problems: A Practical Guide to the Book “Statistics for Chemical and Process Engineers”. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65449-7_2.
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Rogel-Salazar, Jesús. "Alluring Arguments and Ugly Facts – Statistical Modelling and Hypothesis Testing." In Statistics and Data Visualisation with Python. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003160359-6.
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Vidmar, Gaj. "Pixelisation-Based Statistical Visualisation for Categorical Datasets with Spreadsheet Software." In Pixelization Paradigm. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71027-1_5.
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Gower, John C. "Visualisation." In Geometry Driven Statistics. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118866641.ch14.
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Asche, Hartmut, Carolin Kucharczyk, and Marion Simon. "Geodata Discovery Assistant: A Software Module for Rule-Based Cartographic Visualisation and Analysis of Statistical Mass Data." In Computational Science and Its Applications -- ICCSA 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21470-2_41.
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Brezina, Vaclav, and Aina Casaponsa. "Statistics and data visualisation." In Introducing Linguistics. Routledge, 2022. http://dx.doi.org/10.4324/9781003045571-28.
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Young, Joanna, and Jan Wessnitzer. "Descriptive Statistics, Graphs, and Visualisation." In Human–Computer Interaction Series. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26633-6_3.
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Guo, Michael Xin. "Descriptive Statistics and Data Visualisation." In Data Analysis for Business Students. Routledge, 2025. https://doi.org/10.4324/9781003493396-6.
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Rogel-Salazar, Jesús. "The Measure of All Things – Statistics." In Statistics and Data Visualisation with Python. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003160359-4.
Texte intégralActes de conférences sur le sujet "Statistical visualisation"
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Cotta, Romulo, and Robert Cottis. "Methods for the Visualisation of Electrochemical Noise Data." In CORROSION 2007. NACE International, 2007. https://doi.org/10.5006/c2007-07363.
Texte intégralRésumé :
Abstract The use of electrochemical noise (EN) measurements for the study of corrosion processes is now well established, but it remains somewhat unclear as to the best methods to use the extract useful information from the measured time records. Many of the simpler methods are based on the calculation of statistical parameters, such as the standard deviation of current, σI, or potential, σE, and the electrochemical noise resistance, Rn. A problem with many parameters derived from EN measurements is they are inherently noisy, which can make interpretation difficult. This paper explores an alternative method of plotting these parameters that may offer some advantages. The method is inspired by the method developed by Schmitt, and the relationship between this method and conventional statistical parameters is examined.
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Vadakkoot, Raveendran, and Achuth Raghavendra. "Improved image visualisation through statistical techniques." In 2009 4th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2009. http://dx.doi.org/10.1109/iciea.2009.5138631.
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Kapelner, Adam, Peter P. Lee, and Susan Holmes. "An Interactive Statistical Image Segmentation and Visualization System." In International Conference on Medical Information Visualisation - BioMedical Visualisation (MediVis 2007). IEEE, 2007. http://dx.doi.org/10.1109/medivis.2007.5.
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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 teaching. We map out teaching opportunities facilitated by different tool types. Understanding novel data visualisations has become an important element of statistical literacy, and so curricula should expose students to a wide variety of examples.
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Rusu, Adrian, Doru Stoica, Edward Burns, Benjamin Hample, Kevin McGarry, and Robert Russell. "Dynamic Visualizations for Soccer Statistical Analysis." In 2010 14th International Conference Information Visualisation (IV). IEEE, 2010. http://dx.doi.org/10.1109/iv.2010.39.
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Patel, Vijay, Henri Brouquet, Bryan Shaughnessy, and Julian Thomas. "An Investigation of Statistical Visualisation Techniques for Thermal Applications." In 40th International Conference on Environmental Systems. American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-6086.
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Butters, T. D., T. J. Sharpe, S. Guttel, and J. L. Shapiro. "Statistical cluster analysis and visualisation for alarm management configuration." In Asset Management Conference 2014. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.1027.
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Brebric, Marina, Mihaela Vranic, and Damir Pintar. "Streamlining visualisation of geographical data through statistical programming tools." In 2018 First International Colloquium on Smart Grid Metrology (SmaGriMet). IEEE, 2018. http://dx.doi.org/10.23919/smagrimet.2018.8369853.
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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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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.
Texte intégralRésumé :
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.
Rapports d'organisations sur le sujet "Statistical visualisation"
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Gruber, Peter. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/pmgm4wmm7ffer469.
Texte intégralRésumé :
This workshop teaches researchers, from PhD students to professors, how to use ChatGPT and its Advanced Data Analytics tool for statistical analysis without writing a line of code or even knowing how to use a statistics program. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
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Zhan, Peng. Statistical Analysis and Data Visualization in R. Instats Inc., 2022. http://dx.doi.org/10.61700/dizyg5iq1mqj5469.
Texte intégralRésumé :
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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Gruber, Peter H. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/zqir2dzchct5b469.
Texte intégralRésumé :
This 2-day workshop teaches researchers, from PhD students to professors, how to use ChatGPT and its Advanced Data Analytics tool for statistical analysis without writing a line of code or even knowing how to use a statistics program. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
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Gruber, Peter. Using ChatGPT for Advanced Data Analysis 2.0. Instats Inc., 2023. http://dx.doi.org/10.61700/txvjolg6id2hj469.
Texte intégralRésumé :
This 2-day workshop (specifically tailored for the Australian/Asian and US West coast time zones) is aimed at non-statisticians who have to perform data analysis in their work or research. It teaches how to use ChatGPT for statistical analysis without having to know a statistics program or writing a single line of code. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
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Pălici, Bogdan, Alin Savu, Maria Trifon, et al. Mapping - Interactive Tool for Exploring Statistical Data about Cultural Infrastructure in Romania. National Institute for Cultural Research and Training, 2021. http://dx.doi.org/10.61789/mod.cdi.crtg.en.21.
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The Cartographic Module is an instrument for data selection, structuring, visualisation and analysis. It provides for multiple selection functionalities both at geographical area level (county, locality) and at data level (category of indicators and indicators). Three types of dynamic graphs are available on the indicator pages: comparison/evolution, ranking and frequency.
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Meloncelli, Daniel. Preparing and Exploring Data in SPSS (Free Seminar). Instats Inc., 2025. https://doi.org/10.61700/s4pwgrz7xuv2h1479.
Texte intégralRésumé :
This seminar provides hands-on training in SPSS, with a focus on essential data analysis techniques. You will learn to import and define data, recode and group variables, generate descriptive statistics, create visualisations, and conduct assumption tests. Through guided exercises and practical examples, this session will help you to apply SPSS tools effectively, ensuring your data is analysis-ready and your results are robust.
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McCarthy, Noel, Eileen Taylor, Martin Maiden, et al. Enhanced molecular-based (MLST/whole genome) surveillance and source attribution of Campylobacter infections in the UK. Food Standards Agency, 2021. http://dx.doi.org/10.46756/sci.fsa.ksj135.
Texte intégralRésumé :
This human campylobacteriosis sentinel surveillance project was based at two sites in Oxfordshire and North East England chosen (i) to be representative of the English population on the Office for National Statistics urban-rural classification and (ii) to provide continuity with genetic surveillance started in Oxfordshire in October 2003. Between October 2015 and September 2018 epidemiological questionnaires and genome sequencing of isolates from human cases was accompanied by sampling and genome sequencing of isolates from possible food animal sources. The principal aim was to estimate the contributions of the main sources of human infection and to identify any changes over time. An extension to the project focussed on antimicrobial resistance in study isolates and older archived isolates. These older isolates were from earlier years at the Oxfordshire site and the earliest available coherent set of isolates from the national archive at Public Health England (1997/8). The aim of this additional work was to analyse the emergence of the antimicrobial resistance that is now present among human isolates and to describe and compare antimicrobial resistance in recent food animal isolates. Having identified the presence of bias in population genetic attribution, and that this was not addressed in the published literature, this study developed an approach to adjust for bias in population genetic attribution, and an alternative approach to attribution using sentinel types. Using these approaches the study estimated that approximately 70% of Campylobacter jejuni and just under 50% of C. coli infection in our sample was linked to the chicken source and that this was relatively stable over time. Ruminants were identified as the second most common source for C. jejuni and the most common for C. coli where there was also some evidence for pig as a source although less common than ruminant or chicken. These genomic attributions of themselves make no inference on routes of transmission. However, those infected with isolates genetically typical of chicken origin were substantially more likely to have eaten chicken than those infected with ruminant types. Consumption of lamb’s liver was very strongly associated with infection by a strain genetically typical of a ruminant source. These findings support consumption of these foods as being important in the transmission of these infections and highlight a potentially important role for lamb’s liver consumption as a source of Campylobacter infection. Antimicrobial resistance was predicted from genomic data using a pipeline validated by Public Health England and using BIGSdb software. In C. jejuni this showed a nine-fold increase in resistance to fluoroquinolones from 1997 to 2018. Tetracycline resistance was also common, with higher initial resistance (1997) and less substantial change over time. Resistance to aminoglycosides or macrolides remained low in human cases across all time periods. Among C. jejuni food animal isolates, fluoroquinolone resistance was common among isolates from chicken and substantially less common among ruminants, ducks or pigs. Tetracycline resistance was common across chicken, duck and pig but lower among ruminant origin isolates. In C. coli resistance to all four antimicrobial classes rose from low levels in 1997. The fluoroquinolone rise appears to have levelled off earlier and among animals, levels are high in duck as well as chicken isolates, although based on small sample sizes, macrolide and aminoglycoside resistance, was substantially higher than for C. jejuni among humans and highest among pig origin isolates. Tetracycline resistance is high in isolates from pigs and the very small sample from ducks. Antibiotic use following diagnosis was relatively high (43.4%) among respondents in the human surveillance study. Moreover, it varied substantially across sites and was highest among non-elderly adults compared to older adults or children suggesting opportunities for improved antimicrobial stewardship. The study also found evidence for stable lineages over time across human and source animal species as well as some tighter genomic clusters that may represent outbreaks. The genomic dataset will allow extensive further work beyond the specific goals of the study. This has been made accessible on the web, with access supported by data visualisation tools.