Journal articles: 'Statistical Data Interpretation'

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Balaji, SM. "Data interpretation and statistical significance." Indian Journal of Dental Research 30, no. 2 (2019): 163. http://dx.doi.org/10.4103/ijdr.ijdr_363_19.

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Latypova, T. R., and N. Yu Stepanova. "Application Statistical Models for Interpretation Toxicological Data." IOP Conference Series: Earth and Environmental Science 988, no. 4 (February 1, 2022): 042030. http://dx.doi.org/10.1088/1755-1315/988/4/042030.

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Abstract The results of bioassay on infusoria of 75 samples of bottom sediments from 6 water bodies of the Middle Volga region were analyzed using traditional nonparametric methods and statistical models Generalized linear mixed model (GLMM) and Cumulative link mixed model (CLMM). The ambiguity of the interpretation of the results of biotesting performed by nonparametric methods is due to the fact that the toxicological data often do not correspond to the normal distribution. The use of the GLMM and CLMM models allow analyze data that do not correspond to the normal distribution and made it possible to clarify the level of toxicity of a number of ambiguous samples, which, after processing by mathematical model algorithms, acquire the status of either exactly toxic or exactly non-toxic.

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Papaioannou, Agelos, Vasili Simeonov, Panagiotis Plageras, Eleni Dovriki, and Thomas Spanos. "Multivariate statistical interpretation of laboratory clinical data." Open Medicine 2, no. 3 (September 1, 2007): 319–34. http://dx.doi.org/10.2478/s11536-007-0035-1.

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AbstractLaboratory aids are extensively used in the diagnosis of diseases, in preventive medicine, and as management tools. Reference values of clinically healthy people serve as a guide to the clinician in evaluating biochemical parameters. Determination of 21 biochemical parameters of healthy persons using standard methods of analysis. Cluster analysis and principal components analysis were applied on the above 21 biochemical parameters data. The application of a typical classification approach as cluster analysis proved that four major groups of similarity between all 21 clinical parameters are formed, which correspond to the authors assumption of the existence of several summarizing pattern of clinical parameters such as “enzyme,” “major component excretion”, “general health state,” and “blood specific” pattern. These patterns appear also in the subsets obtained by separation of the general dataset into “male”, “female”, “young”, and “adult” healthy groups. The results obtained from principal components analysis have additionally proved the validity of a similar assumption. The intelligent data analysis on the clinical parameter dataset has shown that when a complex system is considered as a multivariate one, the information about the system substantially increases. All these results support an idea that probably a general health indicator could be constructed taking into account the existing classification groups in the list of clinical parameters.

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Chowdhury, Shamsul, Ove Wigertz, and Bo Sundgren. "A Knowledge-based System for Data Analysis and Interpretation." Methods of Information in Medicine 28, no. 01 (January 1989): 6–13. http://dx.doi.org/10.1055/s-0038-1635541.

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Abstract:Traditionally, statistical packages are employed to derive or infer facts about a Universe of Discourse through data analysis and interpretation. It is analysis that serves to transform data into information. Statistical packages provide the users with relatively easy-to-use and powerful mechanics of data analysis, but up to now they do not provide much help with the design and strategies of the analysis. As such, there is a risk of misuse of these packages by statistically inexperienced users. We propose the use of knowledge-based interfaces to support this category of users in statistical evaluations. This paper discusses our experiences from the implementation of a knowledge-based system called MAXITAB. It provides guidance in the processes of data analysis and interpretation and has been programmed as an interface to the statistical package MINITAB.

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Howel, D., R. P. Hirsch, and R. K. Riegelman. "Statistical First Aid: Interpretation of Health Research Data." Biometrics 50, no. 4 (December 1994): 1231. http://dx.doi.org/10.2307/2533472.

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Simeonov, V., I. Stanimirova, and S. Tsakovski. "Multivariate statistical interpretation of coastal sediment monitoring data." Fresenius' Journal of Analytical Chemistry 370, no. 6 (July 1, 2001): 719–22. http://dx.doi.org/10.1007/s002160100863.

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Thompson, W. D. "Statistical criteria in the interpretation of epidemiologic data." American Journal of Public Health 77, no. 2 (February 1987): 191–94. http://dx.doi.org/10.2105/ajph.77.2.191.

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Hurich, C. A., and A. Kocurko. "Statistical approaches to interpretation of seismic reflection data." Tectonophysics 329, no. 1-4 (December 2000): 251–67. http://dx.doi.org/10.1016/s0040-1951(00)00198-0.

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Lee, Dong Kyu. "Data transformation: a focus on the interpretation." Korean Journal of Anesthesiology 73, no. 6 (December 1, 2020): 503–8. http://dx.doi.org/10.4097/kja.20137.

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Several assumptions such as normality, linear relationship, and homoscedasticity are frequently required in parametric statistical analysis methods. Data collected from the clinical situation or experiments often violate these assumptions. Variable transformation provides an opportunity to make data available for parametric statistical analysis without statistical errors. The purpose of variable transformation to enable parametric statistical analysis and its final goal is a perfect interpretation of the result with transformed variables. Variable transformation usually changes the original characteristics and nature of units of variables. Back-transformation is crucial for the interpretation of the estimated results. This article introduces general concepts about variable transformation, mainly focused on logarithmic transformation. Back-transformation and other important considerations are also described herein.

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QUEIROZ, TAMIRES, CARLOS MONTEIRO, LILIANE CARVALHO, and KAREN FRANÇOIS. "INTERPRETATION OF STATISTICAL DATA: THE IMPORTANCE OF AFFECTIVE EXPRESSIONS." STATISTICS EDUCATION RESEARCH JOURNAL 16, no. 1 (May 31, 2017): 163–80. http://dx.doi.org/10.52041/serj.v16i1.222.

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In recent years, research on teaching and learning of statistics emphasized that the interpretation of data is a complex process that involves cognitive and technical aspects. However, it is a human activity that involves also contextual and affective aspects. This view is in line with research on affectivity and cognition. While the affective aspects are recognized as important for the interpretation of data, they were not sufficiently discussed in the literature. This paper examines topics from an empirical study that investigates the influence of affective expression during the interpretation of statistical data by final-year undergraduate students of statistics and pedagogy. These two university courses have different curricular components, which are related to specific goals in the future professional careers of the students. The results suggest that despite differing academic backgrounds in both groups, the participants’ affective expressions were the most frequent type of category used during the interpretation of research assignments. First published May 2017 at Statistics Education Research Journal Archives

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Krewski, D., M. J. Goddard, and D. Murdoch. "Statistical considerations in the interpretation of negative carcinogenicity data." Regulatory Toxicology and Pharmacology 9, no. 1 (February 1989): 5–22. http://dx.doi.org/10.1016/0273-2300(89)90041-x.

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Kenward, Mike. "Missing data in clinical trials: a data interpretation problem with statistical solutions?" Clinical Investigation 2, no. 1 (January 2012): 5–9. http://dx.doi.org/10.4155/cli.11.145.

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Roger, James. "Missing data in clinical trials: a data interpretation problem with statistical solutions?" Clinical Investigation 2, no. 1 (January 2012): 19–24. http://dx.doi.org/10.4155/cli.11.147.

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Hemmings, Robert, and David Wright. "Missing data in clinical trials: a data interpretation problem with statistical solutions?" Clinical Investigation 2, no. 1 (January 2012): 11–17. http://dx.doi.org/10.4155/cli.11.171.

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Молчанова, Татьяна Витальевна. "DIFFICULTIES OF STATISTICAL INTERPRETATION OF CRIME." Вестник Казанского юридического института МВД России, no. 2(52) (June 29, 2023): 90–97. http://dx.doi.org/10.37973/kui.2023.27.17.013.

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Введение: статья посвящена изучению проблем интерпретации статистических данных при проведении криминологического исследования. Автором рассмотрены особенности статистической интерпретации при анализе данных, в том числе связанные с исследовательской проблемой – ошибками в интерпретации статистических данных. Материалы и методы: в качестве эмпирической базы были использованы отдельные формы статистического наблюдения ГИАЦ МВД России, проанализировано состояние учетно-регистрационной дисциплины в органах внутренних дел в период 2021 – 2022 гг. Результаты исследования: приведены определения понятия «интерпретация», имеющего в настоящее время различные научные трактовки. Автором выделены различные подходы к пониманию статистической интерпретации и смежных категорий – толкования и разъяснения. Представлена модель исследовательского процесса состояния преступности, завершающей стадией которого является интерпретация полученных данных. В статье на примере главы 22 УК РФ продемонстрировано, что фиксируемое статистикой незначительное число преступлений в сфере экономической деятельности иногда приводит к их необъективной и недостоверной интерпретации. Обсуждение и заключение: автор пришел к выводу, что объективно и достоверно интерпретированные данные позволяют формировать основные направления предупреждения преступности и использования их в прогнозирующей и управленческой деятельности органов внутренних дел. Ошибки при интерпретации статистических данных формируют научное заблуждение об исследуемой проблеме. statistical data, information, reliability, objectivity, interpretation, interpretation errors, data distortion, crime forecasting, accounting and registration activities

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Bardarov, Ventzislav, Pavlina Simeonova, Ludmila Neikova, Krum Bardarov, Vasil Simeonov, Stefan Tsakovski, and Kamen Kanev. "Statistical interpretation of medical data of patients with alcohol abuse." Open Medicine 7, no. 4 (August 1, 2012): 465–74. http://dx.doi.org/10.2478/s11536-012-0020-1.

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AbstractAn attempt is made to assess a set of biochemical, kinetic and anthropometric data for patients suffering from alcohol abuse (alcoholics) and healthy patients (non-alcoholics). The main goal is to identify the data set structure, finding groups of similarity among the clinical parameters or among the patients. Multivariate statistical methods (cluster analysis and principal components analysis) were used to assess the data collection. Several significant patterns of related parameters were found to be representative of the role of the liver function, kinetic and anthropometric indicators (conditionally named “liver function factor”, “ethanol metabolism factor”, “body weight factor”, and “acetaldehyde metabolic factor”). An effort is made to connect the role of kinetic parameters for acetaldehyde metabolism with biochemical, ethanol kinetic and anthropometric data in parallel.

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Saulnier, George, Janna Castro, and Curtiss Cook. "Statistical Transformation and the Interpretation of Inpatient Glucose Control Data." Endocrine Practice 20, no. 3 (March 2014): 207–12. http://dx.doi.org/10.4158/ep13186.or.

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Banks, H. T., D. F. Kapraun, W. Clayton Thompson, Cristina Peligero, Jordi Argilaguet, and Andreas Meyerhans. "A novel statistical analysis and interpretation of flow cytometry data." Journal of Biological Dynamics 7, no. 1 (December 2013): 96–132. http://dx.doi.org/10.1080/17513758.2013.812753.

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Michiels, S. "S15 Interpretation of microarray data in cancer: a statistical viewpoint." European Journal of Cancer Supplements 5, no. 8 (November 2007): 11. http://dx.doi.org/10.1016/s1359-6349(08)70020-x.

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Wells, B. T., and R. N. Singh. "Statistical interpretation of gate roadways deformation data in the UK." International Journal of Mining Engineering 3, no. 4 (December 1985): 261–70. http://dx.doi.org/10.1007/bf00880838.

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Babanova, Sofia, Orianna Bretschger, Jared Roy, Andrea Cheung, Kateryna Artyushkova, and Plamen Atanassov. "Innovative statistical interpretation of Shewanella oneidensis microbial fuel cells data." Phys. Chem. Chem. Phys. 16, no. 19 (2014): 8956–69. http://dx.doi.org/10.1039/c4cp00566j.

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Raghavan, R. S. "Statistical Interpretation of a Data Adaptive Clutter Subspace Estimation Algorithm." IEEE Transactions on Aerospace and Electronic Systems 48, no. 2 (2012): 1370–84. http://dx.doi.org/10.1109/taes.2012.6178068.

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Shanmugam, Velu Chinnasamy, Pradeepaveerakumari Kumarasamy, and C. Vijayalakshmi. "AN EXHAUSTIVE EMPIRICAL STATISTICAL ANALYSIS AND INTERPRETATION OF BITCOIN DATA." Advances and Applications in Statistics 91, no. 4 (February 12, 2024): 421–37. http://dx.doi.org/10.17654/0972361724022.

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Kim, Sung Hwa, Jihye Lim, and Dae Ryong Kang. "Statistical Methods for Visualizing Healthcare Big Data." Journal of Health Informatics and Statistics 48, Suppl 2 (November 30, 2023): S23—S33. http://dx.doi.org/10.21032/jhis.2023.48.s2.s23.

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With the advancement and acceleration of digital technology, the demand and supply of healthcare big data are increasing. In Korea, the government and companies have made various efforts to utilize healthcare big data, such as deregulation data-related legal regulations and data linkage between different institutions. As a result, many researchers have been able to access a variety of healthcare big data. Although healthcare big data has a vast amount and high value, many researchers are unable to fully access healthcare big data because there are difficulties in processing, analysis, and interpretation for data. The data visualization is recognized as an important tool that can solve these limitations. Using data visualization, researchers can intuitively understand complex data and receive support for decision-making. Additionally, these visualizations promote effective communication between experts in different fields and between experts and non-experts. Visualization is used in a variety of research fields and processes, including data summarization, data exploration, and evaluation and interpretation of predictive models. Various types of visualization have different meanings depending on how they are expressed. Therefore, it is important to express the meaning of visualization appropriately. This study provides representative examples of visual representations for data summarization, data exploration, and predictive model evaluation. This study aimed to improve easier access and utilization of healthcare big data by providing R code and visualization results.

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Meng, Fanfei, and Yuxin Wang. "Transformers: Statistical interpretation, architectures and applications." Applied and Computational Engineering 43, no. 1 (February 26, 2024): 193–210. http://dx.doi.org/10.54254/2755-2721/43/20230832.

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Transformers have been widely recognized as powerful tools to analyze multiple tasks due to its state-of art multi-head attention spaces, such as Natural Language Processing (NLP), Computer Vision (CV) and Speech Recognition (SR). Inspired by its abundant designs and strong functions on analyzing input data, we would like to start from the various architectures, further proceed to the investigation on its statistical mechanism and inference and then introduce its applications on dominant tasks. The underlying statistical mechanisms arouse our interests and intrigue us to investigate it in a higher level, and this surveys will focus on its mathematical foundations and then use the principles to try to analyze the reasons for its excellent performance on many recognition scenarios.

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Thomas, Sabu K., and K. T. Thomachen. "Biodiversity Studies and Multicollinearity in Multivariate Data Analysis." Mapana - Journal of Sciences 6, no. 1 (May 31, 2007): 27–35. http://dx.doi.org/10.12723/mjs.10.2.

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Multicollinearity of explanatory variables often threatens statistical interpretation of ecological data analysis in biodiversity studies. Using litter ants as an example,the impact of multicollinearity on ecological multiple regression and complications arsing from collinearity is explained.We list the various statistical techniques available for enhancing the reliability and interpretation of ecological multiple regressions in the presence of multicollinearity.

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Haroske, G., V. Dimmer, W. Meyer, and K. D. Kunze. "DNA Histogram Interpretation Based on Statistical Approaches." Analytical Cellular Pathology 15, no. 3 (1997): 157–73. http://dx.doi.org/10.1155/1997/935728.

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Image cytometric DNA measurements provide data which are most often interpreted as equivalent to the chromosomal ploidy although the chromosomal and the DNA ploidy are not identical. The common link between them is the cell cycle. Therefore, if destined for DNA ploidy interpretations, the DNA cytometry should be performed on a population‐oriented stochastic basis. Using stochastic sampling the data can be interpreted by applying the rules of stochastic processes. A set of statistical methods is given that enables a DNA histogram to be interpreted objectively and without human interaction. These statistics analyse the precision and accuracy of the entire measurement process. They give in error probabilities for accepting a measurement as reliable, for recognition of stemlines, stemline aneuploidy, and for evaluating so‐called rare events. Nearly 300 image cytometric DNA measurements from breast cancers and rat liver imprints examples have been selected to demonstrate the efficiency of the statistics in each step of interpreting DNA histograms.

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White, Warren H. "Statistical Considerations in the Interpretation of Size-Resolved Particulate Mass Data." Journal of the Air & Waste Management Association 48, no. 5 (May 1998): 454–58. http://dx.doi.org/10.1080/10473289.1998.10463699.

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Eliazar, Iddo I., and Morrel H. Cohen. "On the physical interpretation of statistical data from black-box systems." Physica A: Statistical Mechanics and its Applications 392, no. 13 (July 2013): 2924–39. http://dx.doi.org/10.1016/j.physa.2013.02.021.

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Wrobel, G., F. Chalmel, and M. Primig. "goCluster integrates statistical analysis and functional interpretation of microarray expression data." Bioinformatics 21, no. 17 (July 14, 2005): 3575–77. http://dx.doi.org/10.1093/bioinformatics/bti574.

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Dziak, John Joseph, Lisa C. Dierker, and Beau Abar. "The interpretation of statistical power after the data have been gathered." Current Psychology 39, no. 3 (October 2, 2018): 870–77. http://dx.doi.org/10.1007/s12144-018-0018-1.

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Hsiao, Albert, and Shankar Subramaniam. "Bivariate microarray analysis: statistical interpretation of two-channel functional genomics data." Systems and Synthetic Biology 2, no. 3-4 (December 2008): 95–104. http://dx.doi.org/10.1007/s11693-009-9033-8.

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Yamada, Ryo, Daigo Okada, Juan Wang, Tapati Basak, and Satoshi Koyama. "Interpretation of omics data analyses." Journal of Human Genetics 66, no. 1 (May 8, 2020): 93–102. http://dx.doi.org/10.1038/s10038-020-0763-5.

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AbstractOmics studies attempt to extract meaningful messages from large-scale and high-dimensional data sets by treating the data sets as a whole. The concept of treating data sets as a whole is important in every step of the data-handling procedures: the pre-processing step of data records, the step of statistical analyses and machine learning, translation of the outputs into human natural perceptions, and acceptance of the messages with uncertainty. In the pre-processing, the method by which to control the data quality and batch effects are discussed. For the main analyses, the approaches are divided into two types and their basic concepts are discussed. The first type is the evaluation of many items individually, followed by interpretation of individual items in the context of multiple testing and combination. The second type is the extraction of fewer important aspects from the whole data records. The outputs of the main analyses are translated into natural languages with techniques, such as annotation and ontology. The other technique for making the outputs perceptible is visualization. At the end of this review, one of the most important issues in the interpretation of omics data analyses is discussed. Omics studies have a large amount of information in their data sets, and every approach reveals only a very restricted aspect of the whole data sets. The understandable messages from these studies have unavoidable uncertainty.

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Armstrong, Nicola J., and Mark A. van de Wiel. "Microarray Data Analysis: From Hypotheses to Conclusions Using Gene Expression Data." Analytical Cellular Pathology 26, no. 5-6 (January 1, 2004): 279–90. http://dx.doi.org/10.1155/2004/943940.

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We review several commonly used methods for the design and analysis of microarray data. To begin with, some experimental design issues are addressed. Several approaches for pre‐processing the data (filtering and normalization) before the statistical analysis stage are then discussed. A common first step in this type of analysis is gene selection based on statistical testing. Two approaches, permutation and model‐based methods are explained and we emphasize the need to correct for multiple testing. Moreover, powerful approaches based on gene sets are mentioned. Clustering of either genes or samples is frequently performed when analyzing microarray data. We summarize the basics of both supervised and unsupervised clustering (classification). The latter may be of use for creating diagnostic arrays, for example. Construction of biological networks, such as pathways, is a statistically challenging but complex task that is a relatively new development and hence mentioned only briefly. We finish with some remarks on literature and software. The emphasis in this paper is on the philosophy behind several statistical issues and on a critical interpretation of microarray related analysis methods.

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Walton, Paul H., and Daniel J. Walton. "Simpson’s Paradox in the interpretation of “leaky pipeline” data." International Journal for Transformative Research 3, no. 2 (December 1, 2016): 1–7. http://dx.doi.org/10.1515/ijtr-2016-0013.

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Abstract The traditional ‘leaky pipeline’ plots are widely used to inform gender equality policy and practice. Herein, we demonstrate how a statistical phenomenon known as Simpson’s paradox can obscure trends in gender ‘leaky pipeline’ plots. Our approach has been to use Excel spreadsheets to generate hypothetical ‘leaky pipeline’ plots of gender inequality within an organisation. The principal factors, which make up these hypothetical plots, can be input into the model so that a range of potential situations can be modelled. How the individual principal factors are then reflected in ‘leaky pipeline’ plots is shown. We find that the effect of Simpson’s paradox on leaky pipeline plots can be simply and clearly illustrated with the use of hypothetical modelling and our study augments the findings in other statistical reports of Simpson’s paradox in clinical trial data and in gender inequality data. The findings in this paper, however, are presented in a way, which makes the paradox accessible to a wide range of people.

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Katz, Jonathan N., and Gary King. "A Statistical Model for Multiparty Electoral Data." American Political Science Review 93, no. 1 (March 1999): 15–32. http://dx.doi.org/10.2307/2585758.

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We propose a comprehensive statistical model for analyzing multiparty, district-level elections. This model, which provides a tool for comparative politics research analogous to that which regression analysis provides in the American two-party context, can be used to explain or predict how geographic distributions of electoral results depend upon economic conditions, neighborhood ethnic compositions, campaign spending, and other features of the election campaign or aggregate areas. We also provide new graphical representations for data exploration, model evaluation, and substantive interpretation. We illustrate the use of this model by attempting to resolve a controversy over the size of and trend in the electoral advantage of incumbency in Britain. Contrary to previous analyses, all based on measures now known to be biased, we demonstrate that the advantage is small but meaningful, varies substantially across the parties, and is not growing. Finally, we show how to estimate the party from which each party's advantage is predominantly drawn.

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ROGERS, DAVID J., and LUIGI SEDDA. "Statistical models for spatially explicit biological data." Parasitology 139, no. 14 (October 19, 2012): 1852–69. http://dx.doi.org/10.1017/s0031182012001345.

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SUMMARYExisting algorithms for predicting species' distributions sit on a continuum between purely statistical and purely biological approaches. Most of the existing algorithms are aspatial because they do not consider the spatial context, the occurrence of the species or conditions conducive to the species' existence, in neighbouring areas. The geostatistical techniques of kriging and cokriging are presented in an attempt to encourage biologists more frequently to consider them. Unlike deterministic spatial techniques they provide estimates of prediction errors. The assumptions and applications of common geostatistical techniques are presented with worked examples drawn from a dataset of the bluetongue outbreak in northwest Europe in 2006. Emphasis is placed on the importance and interpretation of weights in geostatistical calculations. Covarying environmental data may be used to improve predictions of species’ distributions, but only if their sampling frequency is greater than that of the species’ or disease data. Cokriging techniques are unable to determine the biological significance or importance of such environmental data, because they are not designed to do so.

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Levy, Paul S. "Reviews, Notes, and Listings: Statistical First Aid: Interpretation of Health Research Data." Annals of Internal Medicine 119, no. 3 (August 1, 1993): 254. http://dx.doi.org/10.7326/0003-4819-119-3-199308010-00037.

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Byrd, D. M. "Symposium VI: New Statistical Approaches to the Qualitative Interpretation of Toxicology Data." Journal of the American College of Toxicology 7, no. 5 (September 1988): 559–63. http://dx.doi.org/10.3109/10915818809019532.

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This symposium describes some new statistical methods of use in the analysis of toxicology data for purposes of hazard evaluation, particularly as related to the qualitative phase of carcinogen risk assessment. C.J. Portier discusses life table analysis of cancer bioassays. Current assumptions about the lethality of treatment and tumor can lead to incorrect interpretation of results. D.W. North provides an introduction to the concepts of Bayesian statistics and shows how the application of judgmental probabilities can assist in reaching broad conclusions. L.B. Lave and G.S. Omenn apply a Bayesian method to estimate the likelihood of bioassay outcomes given short-term test data. The best screening test is not necessarily the most accurate or the least expensive. W.P. Darby and J.K. Gohagan use a bayesian method to integrate data from different bioassays of the same substance. In a case study of saccharin, they show that the totality of results can lead to conclusions that differ from those achieved with a single bioassay.

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Sauerbrei, W., M. Blettner, and M. Schumacher. "The Importance of Basic Statistical Principles for the Interpretation of Epidemiological Data." Oncology Research and Treatment 20, no. 6 (1997): 455–60. http://dx.doi.org/10.1159/000219007.

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Hill, Lowell D., and Takashi Takayama. "ECONOMIC INTERPRETATION OF STATISTICAL TESTS OF SIMILARITIES BETWEEN TWO SETS OF DATA." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 19, no. 1 (November 13, 2008): 138–45. http://dx.doi.org/10.1111/j.1744-7976.1971.tb01144.x.

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Vujević, Slavko, and Mate Kurtović. "Statistical analysis and interpretation of resistivity sounding data in earthing grid design." Mathematics and Computers in Simulation 50, no. 1-4 (November 1999): 339–49. http://dx.doi.org/10.1016/s0378-4754(99)00078-6.

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Cardoso, Manoel F., George C. Hurtt, Berrien Moore, Carlos A. Nobre, and Heather Bain. "Field work and statistical analyses for enhanced interpretation of satellite fire data." Remote Sensing of Environment 96, no. 2 (May 2005): 212–27. http://dx.doi.org/10.1016/j.rse.2005.02.008.

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Şerbănescu, Cristina, and Cosmina-Elena Pop. "Data analysis and statistical estimation for time series: improving presentation and interpretation." Soft Computing 21, no. 14 (January 25, 2016): 3919–30. http://dx.doi.org/10.1007/s00500-016-2041-1.

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Guerra-Castro, Edlin, Carlos Carmona-Suárez, and Jesús E. Conde. "Biotelemetry of crustacean decapods: sampling design, statistical analysis, and interpretation of data." Hydrobiologia 678, no. 1 (August 7, 2011): 1–15. http://dx.doi.org/10.1007/s10750-011-0828-8.

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Alcalde, Juan, Clare E. Bond, and Charles H. Randle. "Framing bias: The effect of figure presentation on seismic interpretation." Interpretation 5, no. 4 (November 30, 2017): T591—T605. http://dx.doi.org/10.1190/int-2017-0083.1.

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Interpreters of reflection seismic data generally use images to disseminate the outcomes of their geologic interpretation work. The presentation of such interpretation images can generate unwanted biases in the perception of the observers, an effect known as “framing bias.” These framing biases can enhance or reduce the confidence of the observer in the presented interpretation, independently of the quality of the seismic data or the geologic interpretation. We have tested the effect of presentation on confidence in interpretation of 761 participants of an online experiment. Experiment participants were presented with seismic images and interpretations, deliberately modified in different aspects to introduce potential framing biases. Statistical analysis of the results indicates that the image presentation had a subdued effect on participants’ confidence compared with the quality of the seismic data and interpretation. The results allow us to propose recommendations to minimize biases in the observers related to the presentation of seismic interpretations: (1) interpretations should be shown with the seismic data in the background to ease comparison between the uninterpreted-interpreted data and the subsequent confidence assessments; (2) seismic data displayed in color aids in the interpretation, although the color palettes must be carefully chosen to prevent unwanted bias from common color spectrum in the observers; and (3) explicit indication of uncertainty by the interpreters in their own interpretation, which was deemed useful by the participants.

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Jafari, Faezeh, and Sattar Dorafshan. "Bridge Inspection and Defect Recognition with Using Impact Echo Data, Probability, and Naive Bayes Classifiers." Infrastructures 6, no. 9 (September 14, 2021): 132. http://dx.doi.org/10.3390/infrastructures6090132.

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Interpretation of IE data have been carried out by analyzing IE signals in frequency domain to determine the maximum frequency. However, the current peak frequency method can be inaccurate. The purpose of this research is to introduce features in IE signals that can be used for effective classification and interpretation for bridge deck evaluation through statistical analysis and Naive Bayes classifiers. The dataset contained IE data collected from eight slabs created at Advanced Sensing Technology FAST NDE laboratory (FHWA). A set of statistical features in time domain, normalized peak values, and length of preprocessed signals were used to classify the IE data, statistically. Then, Naive Bayes classifiers was employed to recognize defect area. Finally, the result of statistical classification was compared with frequency approach. The result shows that 19 and 21% of the IE signals collected from the defect area have multiple peaks, respectively. However, 85% of the IE signals collected from the sound set had only one peak. A probability classifier was used to find the relationship between the result of the frequency method and statistical analysis. The result shows that 10% of the IE signals were usable for estimating the thickness in the sound group.

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Lowell, Richard B., Kathleen Hedley, and Edward Porter. "Data Interpretation Issues for Canada's Environmental Effects Monitoring Program." Water Quality Research Journal 37, no. 1 (February 1, 2002): 101–17. http://dx.doi.org/10.2166/wqrj.2002.007.

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Abstract As part of Canada's National Environmental Effects Monitoring (EEM) Program, regulated pulp and paper mills are (and metal mines will be) required to submit an interpretive report describing monitoring results. General guidance has been prepared on how to interpret these EEM data—specifically: 1) which effect endpoints to use, 2) the statistical (or other) approach to use for each endpoint to determine the presence or absence of an effect associated with exposure, and 3) the role of power analysis, α, β, and effect size in determining effects. A statistically significant difference (relative to reference conditions) in any of the effect endpoints is to be considered an exposure-associated effect for the purposes of warranting possible follow-up action. Such an effect does not, however, necessarily indicate ecological, social, or economic significance sufficient to require corrective action. Power analyses should be conducted both at the beginning of a study to determine required sampling effort and at the end of a study to determine whether the power that was actually achieved was sufficient to detect the effect size of interest. A key recommendation is to set α = β as a starting point for data interpretation. The initial recommendations of the general guidance are expected to evolve as environmental effects become better understood.

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PRODROMOU, THEODOSIA. "STATISTICAL LITERACY IN DATA REVOLUTION ERA: BUILDING BLOCKS AND INSTRUCTIONAL DILEMMAS." STATISTICS EDUCATION RESEARCH JOURNAL 16, no. 1 (May 31, 2017): 38–43. http://dx.doi.org/10.52041/serj.v16i1.212.

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The data revolution has given citizens access to enormous large-scale open databases. In order to take into account the full complexity of data, we have to change the way we think in terms of the nature of data and its availability, the ways in which it is displayed and used, and the skills that are required for its interpretation. Substantial changes in the content and processes involved in statistics education are needed. This paper calls for the introduction of new pedagogical constructs and principles needed in the age of the data revolution. The paper deals with a new construct of statistical literacy. We describe principles and dispositions that will become the building blocks of our pedagogical model. Our model suggests that effective engagement with large-scale data, modelling and interpretation situations requires the presence of knowledge-bases as well as supporting dispositions. First published May 2017 at Statistics Education Research Journal Archives

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Jarc, Simona. "Statistical approach to interpretation of geochemical data of stream sediment in Pleše mining area." Geologija 65, no. 2 (December 21, 2022): 225–35. http://dx.doi.org/10.5474/geologija.2022.013.

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The Ba, Pb and Zn ore deposit Pleše near Ljubljana is one of the formerly productive mines. The stream sediments were sampled and analysed by XRF to establish the effect of grain size, mineralization, and downstream location of sampling sites on geochemical composition based on various statistical analyses. Statistical analyses of the geochemical data confirm the impact of mineralization. The parametric t-test, non-parametric Mann-Whitney test and cluster analysis showed only minor differences in the geochemical composition of the samples with different grain sizes (< 0.063 mm and 0.063-2 mm). The parametric and non-parametric correlation coefficients as well as cluster analysis indicate that the contents of Si, Al, K, Rb, and Fe are associated with weathered rock forming minerals such as micas, and clay minerals, whereas Nb and Zr are associated with minerals resistant to weathering. Ca and Mg are associated with carbonates. S, Ba, Sr, Pb, Zn, and Mn indicate local mineralization with sulphates and sulphides. The results of the t-test and analysis of variance, Mann-Whitney tests and Kruskal-Wallis ANOVA of the groups established by the cluster analysis confirm that the contents of Ba, Pb and Sr have a statistically significant influence on the classification of the cluster group - i.e., the influence of sediment mineralization. There are no differences in elemental contents in the sediment samples downstream. The statistical approach to evaluate the geochemical data has proven useful and provides a good basis for further interpretation.

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Journal articles on the topic 'Statistical Data Interpretation'

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