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Relevant bibliographies by topics / Correlation (Statistics)

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Academic literature on the topic 'Correlation (Statistics)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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Journal articles on the topic "Correlation (Statistics)"

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McClure, Philip. "Correlation Statistics." Journal of Hand Therapy 18, no. 3 (July 2005): 378–80. http://dx.doi.org/10.1197/j.jht.2005.04.015.

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Curran-Everett, Douglas. "Explorations in statistics: correlation." Advances in Physiology Education 34, no. 4 (December 2010): 186–91. http://dx.doi.org/10.1152/advan.00068.2010.

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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This sixth installment of Explorations in Statistics explores correlation, a familiar technique that estimates the magnitude of a straight-line relationship between two variables. Correlation is meaningful only when the two variables are true random variables: for example, if we restrict in some way the variability of one variable, then the magnitude of the correlation will decrease. Correlation cannot help us decide if changes in one variable result in changes in the second variable, if changes in the second variable result in changes in the first variable, or if changes in a third variable result in concurrent changes in the first two variables. Correlation can help provide us with evidence that study of the nature of the relationship between x and y may be warranted in an actual experiment in which one of them is controlled.

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Walizada, Sayeeda. "Significance of correlation in statistics." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 6 (2021): 317–18. http://dx.doi.org/10.54660/.ijmrge.2021.2.6.317-318.

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Correlation quantifies the degree and direction to which two variables are related. Correlation does not fit a line through the data points. The sign (+, -) of the correlation coefficient indicates the direction of the association. The magnitude of the correlation coefficient indicates the strength of the association, e.g. A correlation of r = - 0.8 suggests a strong, negative association (reverse trend) between two variables, whereas a correlation of r = 0.4 suggest a weak, positive association. A correlation close to zero suggests no linear association between two continuous variables. Linear regression finds the best line that predicts dependent variable from independent variable. The decision of which variable calls dependent and which calls independent is an important matter in regression, as it'll get a different best-fit line if you swap the two. The line that best predicts independent variable from dependent variable is not the same as the line that predicts dependent variable from independent variable in spite of both those lines have the same value for R2. Linear regression quantifies goodness of fit with R2, if the same data put into correlation matrix the square of r degree from correlation will equal R2 degree from regression. The sign (+, -) of the regression coefficient indicates the direction of the effect of independent variable(s) into dependent variable, where the degree of the regression coefficient indicates the effect of the each independent variable into dependent variable.

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Gibbons, Steven J. "The optimal correlation detector?" Geophysical Journal International 228, no. 1 (August 23, 2021): 355–65. http://dx.doi.org/10.1093/gji/ggab344.

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SUMMARY Correlation detectors are now used routinely in seismology to detect occurrences of signals bearing close resemblance to a reference waveform. They facilitate the detection of low-amplitude signals in significant background noise that may elude detection using energy detectors, and they associate a detected signal with a source location. Many seismologists use the fully normalized correlation coefficient C between the template and incoming data to determine a detection. This is in contrast to other fields with a longer tradition for matched filter detection where the theoretically optimal statistic C2 is typical. We perform a systematic comparison between the detection statistics C and C|C|, the latter having the same dynamic range as C2 but differentiating between correlation and anticorrelation. Using a database of short waveform segments, each containing the signal on a 3-component seismometer from one of 51 closely spaced explosions, we attempt to detect P- and S-phase arrivals for all events using short waveform templates from each explosion as reference signals. We present empirical statistics of both C and C|C| traces and demonstrate that C|C| detects confidently a higher proportion of the signals than C without evidently increasing the likelihood of triggering erroneously. We recall from elementary statistics that C2, also called the coefficient of determination, represents the fraction of the variance of one variable which can be explained by another variable. This means that the fraction of a segment of our incoming data that could be explained by our signal template decreases almost linearly with C|C| but diminishes more rapidly as C decreases. In most situations, replacing C with C|C| in operational correlation detectors may improve the detection sensitivity without hurting the performance-gain obtained through network stacking. It may also allow a better comparison between single-template correlation detectors and higher order multiple-template subspace detectors which, by definition, already apply an optimal detection statistic.

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Joe, George W., and Jorge L. Mendoza. "The Internal Correlation: Its Applications in Statistics and Psychometrics." Journal of Educational Statistics 14, no. 3 (September 1989): 211–26. http://dx.doi.org/10.3102/10769986014003211.

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The internal correlation, a measure of dependency in a set of variables, is discussed and generalized. This coefficient is an upper bound to the product moment correlations, multiple correlations, and canonical correlations that can be defined in a set of variables. Applications of the internal correlation coefficient and its generalizations are given for a number of data-analytic situations. Where appropriate, we discuss tests of significance. We illustrate the internal correlation and expand the concept to a series of additional indices: local internal, up-internal, and down-internal correlations. Uses of these indices are illustrated in several areas: multicollinearity, ridge regression, factor analysis, principal components analysis, and test reliability.

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Zhuo, Bin, Duo Jiang, and Yanming Di. "Test-statistic correlation and data-row correlation." Statistics & Probability Letters 167 (December 2020): 108903. http://dx.doi.org/10.1016/j.spl.2020.108903.

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YAMAKI, Shunsuke, Masahide ABE, and Masayuki KAWAMATA. "Statistical Analysis of Phase-Only Correlation Functions Based on Directional Statistics." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E97.A, no. 12 (2014): 2601–10. http://dx.doi.org/10.1587/transfun.e97.a.2601.

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Wang, Jinling, Ali Almagbile, Youlong Wu, and Toshiaki Tsujii. "Correlation Analysis for Fault Detection Statistics in Integrated GNSS/INS Systems." Journal of Global Positioning Systems 11, no. 2 (December 31, 2012): 89–99. http://dx.doi.org/10.5081/jgps.11.2.89.

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Zirmansyah, Zirmansyah. "Kualitas Skripsi Mahasiswa Universitas Al Azhar Indonesia: Pengaruh Hasil Belajar Metodologi Penelitian dan Statistik terhadap Kualitas Skripsi." JURNAL Al-AZHAR INDONESIA SERI HUMANIORA 1, no. 1 (April 4, 2011): 19. http://dx.doi.org/10.36722/sh.v1i1.20.

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The objective of the research is to study the relationship between learning outcome on statistic, learning outcome on research methodology and thesis quality. The research was carried out at the student Al Azhar University, with 53 samples of thesis which were selected randomly. The research concludes there is positive correlation between: (1) learning outcome on statistics and thesis quality; (2) knowledge on research methodology and thesis quality; (3) furthermore, there is a positive correlation between learning outcome on statistcs, learning outcome on research methodology, with thesis quality. Therefore thesis quality can be increased by improving learning outcome on statistic, and learning outcome on research metodhology.

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Schaff, D. P. "Semiempirical Statistics of Correlation-Detector Performance." Bulletin of the Seismological Society of America 98, no. 3 (June 1, 2008): 1495–507. http://dx.doi.org/10.1785/0120060263.

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Dissertations / Theses on the topic "Correlation (Statistics)"

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Conrad, III Dallis G. "Speckle Statistics of Articulating Objects." University of Dayton / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1320673424.

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Abdalmajid, Mohammed Babekir Elmalik. "An application of factor analysis on a 24-item scale on the attitudes towards AIDS precautions using Pearson, Spearman and Polychoric correlation matrices." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8765_1184324798.

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The 24-item scale has been used extensively to assess the attitudes towards AIDS precautions. This study investigated the usefulness and validity of the instrument in a South African setting, fourteen years after the development of the instrument. If a new structure could be found statistically, the HIV/AIDS prevention strategies could be more effective in aiding campaigns to change attitudes and sexual behaviour.

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Wood, Christopher. "Higher order statistics in photon-correlation spectroscopy." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267626.

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Penman, David Binnie. "Random graphs with correlation structure." Thesis, University of Sheffield, 1998. http://etheses.whiterose.ac.uk/14768/.

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In this thesis we consider models of random graphs where, unlike in the classical models G (n, p) the probability of an edge arising can be correlated with that of other edges arising. Attention focuses on graphs whose vertices are each assigned a colour (type) at random and where edges between differently coloured vertices subsequently arise with different probabilities (so-called RRC graphs), especially the special case with two colours. Various properties of these graphs are considered, often by comparing and contrasting them with the classical model with the same probability of each particular edge existing. Topics examined include the probabilities of trees and cycles, how the joint probability of two subgraphs compares with the product of their probabilities, the number of edges in the graph (including large deviations results), connectedness, connectivity, the number and order of complete graphs and cliques, and tournaments with correlation structure.

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Smoljanovic, Lada. "The estimation of intraclass correlation coefficients." Thesis, University of Hull, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301491.

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Fridline, Mark M. "Almost Sure Confidence Intervals for the Correlation Coefficient." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1258999665.

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Thesis(Ph.D.)--Case Western Reserve University, 2010
Title from PDF (viewed on 2009-12-22) Department of Statistics Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center

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Pei, Yanbo. "Statistical inference for correlated binary data from bilateral studies." HKBU Institutional Repository, 2009. http://repository.hkbu.edu.hk/etd_ra/1077.

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Hamdi, Walaa Ahmed. "Local Distance Correlation: An Extension of Local Gaussian Correlation." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1589239468129597.

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Vargas-Guzman, Jose Antonio 1961. "Scaling variances, correlation and principal components with multivariate geostatistics." Diss., The University of Arizona, 1998. http://hdl.handle.net/10150/282813.

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A new concept of dispersion (cross) covariance has been introduced for the modeling of spatial scale dependent multivariate correlations. Such correlations between attributes depend on the spatial size of the domain and size of samples in the population and have been modeled by first time in this research. Modeled correlations have been used to introduce a new scale dependent principal component analysis (PCA) method. This method is based on computation of eigen values and vectors from dispersion covariance matrices or scale dependent correlations which can be modeled from integrals of matrix variograms. For second order stationary random functions this PCA converges for large domains to the classic PCA. A new technique for computing variograms from spatial variances have also been developed using derivatives. For completeness, a deeper analysis of the linear model of coregionalizations widely used in multivariate geostatistics has been included as well. This last part leads to a new more sophisticated model we termed "linear combinations coregionalization model." This whole research explains the relationship between different average states and the micro- state of vector random functions in the framework of geostatistics. Examples have been added to illustrate the practical application of the theory. This approach will be useful in all earth sciences and particularly in soil and environmental sciences.

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Ma, Ping Hang. "Disagreement : estimation of relative bias or discrepancy rate." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26445.

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Not only basic research in sciences, but also medicine, law, and manufacturing need statistical techniques, including graphics, to assess disagreement. For some items or individuals ⍳ = 1,2,---,ո suppose that pairs (X⍳,Y⍳) denote each item's measurements by two distinct methods or by two observers, or X⍳ and Y⍳ may be initial and repeat measurement scores, with discrepancy D⍳ = X⍳ - Y⍳. Disagreement may be characterized by location and scale parameters of discrepancy distributions. The present work primarily addresses estimation of central tendency - relative bias or median discrepancy (or discrepancy rate in some instances). Most previous literature on "agreement" or "reliability" instead concerns X, Y correlation, which can be regarded as the complement of discrepancy variance. (There is ambiguity or confusion about concepts of "reliability" in the literature of various applications.) Discrepancies D₁, D₂, • • •, Dո in practice often violate assumptions of standard statistical models and methods that have been commonly applied in studies of agreement. In particular, both X⍳ and Y⍳ generally incorporate measurement errors. Further, these two measurement error distributions for the ⍳th item need not be the same; and both distributions could depend on the magnitude µ⍳, of the item being measured. Hence, for example, discrepancy D⍳ could have variance proportional to the size of the item; and in general D₁, D₂, • • •, Dո are not identically distributed. Finally, the selection of items ⍳ = 1,2, • • •, ո often is not random. To estimate median discrepancy, we consider nonparametric confidence intervals corresponding to Student t test, sign test, Wilcoxon signed rank test, or other permutation tests. Several criteria are developed to compare the performance of one procedure relative to another, including expected ratio of confidence interval lengths (related to Pitman asymptotic relative efficiency of tests) and relative variability of interval lengths. Theoretical calculations and Monte Carlo simulation results suggest different procedural preferences for random sampling from different distributions. For discrepancies distributed non-identically, but symmetrically about a common median value, mixture sampling is used as an approximate model. This approach is related to a "random walk" (rather than random sample) model of D₁, D₂, • • •, Dո proposed particularly for discrepancies between counting processes. We also emphasize graphic methods, especially plots of difference of Y - X versus average (X + Y)/2, for exploratory analysis of discrepancy data and to choose appropriate statistical models and numerical methods. Various data sets are analyzed as examples of the methodology.
Science, Faculty of
Statistics, Department of
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Books on the topic "Correlation (Statistics)"

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Maurice, Kendall. Rank correlation methods. 5th ed. London: Edward Arnold, 1990.

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Kendall, Maurice G. Rank correlation methods. 5th ed. London: E. Arnold, 1990.

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Burg, Eeke Van der. Nonlinear canonical correlation and some related techniques. Leiden: DSWO Press, 1988.

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Pawlowsky-Glahn, Vera. Compositional data analysis: Theory and applications. Hoboken, N.J: Wiley, 2011.

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Rönz, Bernd. Regressions- und Korrelationsanalyse: Grundlagen, Methoden, Beispiele. Wiesbaden: Gabler, 1992.

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1939-, Bhaskara Rao B., ed. Cointegration for the applied economist. New York: St. Martin's Press, 1994.

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1939-, Bhaskara Rao B., ed. Cointegration for the applied economist. 2nd ed. New York: Palgrave Macmillan, 2008.

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1939-, Bhaskara Rao B., ed. Cointegration for the applied economist. 2nd ed. New York: Palgrave Macmillan, 2008.

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1939-, Bhaskara Rao B., ed. Cointegration for the applied economist. 2nd ed. Basingstoke: Palgrave Macmillan, 2007.

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1963-, Rangarajan G., and Ding Mingzhou, eds. Processes with long-range correlations: Theory and applications. Berlin: Springer, 2003.

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Book chapters on the topic "Correlation (Statistics)"

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Sabine, P., and C. Plumpton. "Correlation." In Statistics, 64–74. London: Palgrave Macmillan UK, 1985. http://dx.doi.org/10.1007/978-1-349-07668-0_6.

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Ball, A. D., and G. D. Buckwell. "Correlation." In Statistics A Level, 202–20. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-13856-2_13.

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Rhinehart, R. Russell, and Robert M. Bethea. "Correlation." In Applied Engineering Statistics, 263–74. 2nd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003222330-16.

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Chamberlin, Scott A. "Correlation." In Statistics for Kids, 69–82. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238201-6.

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Schiefer, Hartmut, and Felix Schiefer. "Correlation." In Statistics for Engineers, 95–98. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32397-4_6.

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Rossman, Allan J., and Beth L. Chance. "Correlation Coefficient." In Workshop Statistics, 127–44. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2926-9_8.

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Buckley, James J. "Fuzzy Correlation." In Fuzzy Statistics, 103–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39919-3_21.

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Schumacker, Randall, and Sara Tomek. "Correlation." In Understanding Statistics Using R, 209–18. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6227-9_12.

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Vidakovic, Brani. "Correlation." In Springer Texts in Statistics, 571–97. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0394-4_15.

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Urdan, Timothy C. "Correlation." In Statistics in Plain English, 171–90. 5th ed. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003006459-12.

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Conference papers on the topic "Correlation (Statistics)"

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Wang, Wei, Steen G. Hanson, and Mitsuo Takeda. "Statistics of polarization speckle: theory versus experiment." In Correlation Optics 2009. SPIE, 2009. http://dx.doi.org/10.1117/12.855761.

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Kurashov, Vitalij N., and Andrey V. Kurashov. "Inverse problem in 2D photocounting statistics." In International Conference on Correlation Optics, edited by Oleg V. Angelsky. SPIE, 1997. http://dx.doi.org/10.1117/12.295703.

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Shaumeyer, J. N., and Robert W. Gammon. "Optimizing Fitting Statistics in Photon Correlation Spectroscopy." In Photon Correlation Techniques and Applications. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/pcta.1988.pcmdr14.

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We have performed an experiment to test our understanding of the run time, T, necessary to achieve a specified precision in the value of the intensity coherence time, τc, extracted from correlation functions taken in the strong signal limit, and to test predictions for the values of some experimental parameters that optimize the precision. Using ensembles of 10 correlation functions taken at 5 different choices of sample time, we found that the ensemble estimators for the error in τc were well described by the expression δτ/τ c =4.2/T/τ c , in agreement with the work of Degiorgio and Lastovka (1971). The sample times used were chosen so that the number of coherence times spanned by the 128 channels of the correlator, α, covered the range 1 ≤ α ≤ 16; in this range, we found no evidence of a minimum in δτ/τc to suggest an optimum value of α. These results were independent of whether we used three-parameter or two-parameter least-squares fits to extract τc. However, we did find that the two fits gave systematically different values of τc, and both show a similar dependence on α.

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Pan, Zhongbao, Zuishuang Luo, Ying Wang, and Wei Wang. "Statistics modeling of random polarization in K-distributed sea clutter." In International Conference Correlation Optics (COR2023), edited by Oleg V. Angelsky and Claudia Yu Zenkova. SPIE, 2024. http://dx.doi.org/10.1117/12.3013059.

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Lading, Lars, J. Adin Mann, and R. V. Edwards. "Photon Statistics of Light Scattered by a Liquid Gas Interface." In Photon Correlation Techniques and Applications. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/pcta.1988.pcs163.

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The photon statistics of light scattered by a surface is investigated. It is shown how the photon correlation function depends on the mode of the light scattering configuration and on the space-time correlation of the surface structure. The uncertainties of the estimated photon correlation are evaluated.

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Luo, Zuishuang, Zhongbao Pan, Ying Wang, and Wei Wang. "Statistics of random polarization in sea clutter with Weibull-distribution-intensity." In International Conference Correlation Optics (COR2023), edited by Oleg V. Angelsky and Claudia Yu Zenkova. SPIE, 2024. http://dx.doi.org/10.1117/12.3009732.

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Lohmann, Gabriele, Johannes Stelzer, Verena Zuber, Tilo Buschmann, Michael Erb, and Klaus Scheffler. "Correlation bundle statistics in fMRI data." In 2014 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2014. http://dx.doi.org/10.1109/prni.2014.6858529.

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Hanson, Steen G., Rene S. Hansen, and Harold T. Yura. "Statistics for partially developed speckles: the impact on speckle-based measurements." In Fifth International Conference on Correlation Optics, edited by Oleg V. Angelsky. SPIE, 2002. http://dx.doi.org/10.1117/12.455196.

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Zdrajevsky, R. A., O. V. Ushakova, and D. A. Zimnyakov. "Asymptotic behavior of the higher-order statistics of time-integrated speckle patterns and sensitivity of full-field speckle techniques to scatter mobility." In Correlation Optics 2011, edited by Oleg V. Angelsky. SPIE, 2011. http://dx.doi.org/10.1117/12.920885.

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Ma, Ning, Steen G. Hanson, Mitsuo Takeda, and Wei Wang. "Statistics of dynamic polarization speckle generated from a moving rough-surfaced retardation plate." In Fourteenth International Conference on Correlation Optics, edited by Oleg V. Angelsky. SPIE, 2020. http://dx.doi.org/10.1117/12.2540027.

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Reports on the topic "Correlation (Statistics)"

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Bhatt, Nikita. Descriptive statistics 2: correlation and regression. BJUI Knowledge, April 2022. http://dx.doi.org/10.18591/bjuik.0758.

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Oberer, Richard B. Fission Multiplicity Detection with Temporal Gamma-Neutron Discrimination from Higher-Order Time Correlation Statistics. Office of Scientific and Technical Information (OSTI), October 2002. http://dx.doi.org/10.2172/808851.

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Oberer, R. B. Fission Multiplicity Detection With Temporal Gamma-Neutron Discrimination From Higher Order Time Correlation Statistics. Office of Scientific and Technical Information (OSTI), January 2002. http://dx.doi.org/10.2172/814311.

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Panchenko, Liubov, and Andrii Khomiak. Education Statistics: Looking for Case-Study for Modeling. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4461.

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The article deals with the problem of using modeling in social statistics courses. It allows the student-researcher to build one-dimensional and multidimensional models of the phenomena and processes that are being studied. Social Statistics course programs from foreign universities (University of Arkansas; Athabasca University; HSE University, Russia; McMaster University, Canada) are analyzed. The article provides an example using the education data set – Guardian UK universities ranking in Social Statistics course. Examples of research questions are given, data analysis for these questions is performed (correlation, hypothesis testing, discriminant analysis). During the research the discriminant model with group variable – modified Guardian score – and 9 predictors: course satisfaction, teaching quality, feedback, staff-student ratio, money spent on each student and other) was built. Lower student’s satisfaction with feedback was found to be significantly different from the satisfaction with teaching. The article notes the modeling and statistical analysis should be accompanied by a meaningful interpretation of the results. In this example, we discussed the essence of university ratings, the purpose of Guardian rating, the operationalization and measurement of such concepts as satisfaction with teaching, feedback; ways to use statistics in education, data sources etc. with students. Ways of using this education data in group and individual work of students are suggested.

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Nuttall, Albert H. Second-Order Statistics of Spectral and Correlation Estimates Obtained by Means of Weighted Overlapped FFT Processing. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada439705.

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KHUDALOVA, M., V. FILONENKO, and E. KUDZOEVA. PSYCHOSOMATICS IN CONNECTION WITH THE AFFECTIVE DISORDERS OF PERSONALITY. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-4-2-365-374.

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In recent years, we can notice a significant increase in psychosomatic disorders among borderline mental pathology, which are reasonably considered “the pathology of modern civilization”. The purpose of this study is to identify the relationship between psychosomatic disorders and the affective disorders of the personality. The study used the following methods: a diagnostic conversation and analysis of medical documents with the results of clinical examination, a scale for psychological express diagnostics of semi-structured depressive disorders (based on MMPI), a self-assessment scale by Ch.D. Spielberger - Yu.L. Hanin, Toronto Alexithymia Scale (TAS). Statistical methods of processing the empirical research results in the SPSS 22.0 program: descriptive statistics, correlation analysis (p-Spearman’s rank correlation). As a result of the study we can assert that psychosomatic disorders in respondents in the form of functional pathology of various organs and systems are connected with affective disorders in the form of moderate or severe depression of a neurotic level of various origins, alexithymia and high personal anxiety.

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De Groote, Sandra L., Jung Mi Scoulas, Paula R. Dempsey, Deborah D. Blecic, and Felicia A. Barrett. Library Impact Research Report: Faculty Publication Patterns at a Large Urban University and Correlation with Collections Use and Size. Association of Research Libraries, June 2022. http://dx.doi.org/10.29242/report.uillinoischicago2022.

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As part of ARL’s Research Library Impact Framework initiative, the University of Illinois Chicago (UIC) conducted a study to demonstrate how the availability and use of library collections impacts faculty productivity and publication patterns over time. To address these questions, the project used various statistics: collection size (measured by journal holdings), collection use (measured by number of references in the publications), number of publications by faculty, publication impact (measured by number of citations), number of co-authors, grant funding, page counts, and faculty demographic information.

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Soloviev, Vladimir N., Symon P. Yevtushenko, and Viktor V. Batareyev. Comparative analysis of the cryptocurrency and the stock markets using the Random Matrix Theory. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3681.

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This article demonstrates the comparative possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency and developed stock market. Then, combining the empirical cross-correlation matrix with the Random Matrix Theory, we mainly examine the statistical properties of cross-correlation coefficients, the evolution of the distribution of eigenvalues and corresponding eigenvectors in both markets using the daily returns of price time series. The result has indicated that the largest eigenvalue reflects a collective effect of the whole market, and is very sensitive to the crash phenomena. It has been shown that introduced the largest eigenvalue of the matrix of correlations can act like indicators-predictors of falls in both markets.

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Olefirenko, Nadiia V., Ilona I. Kostikova, Nataliia O. Ponomarova, Kateryna O. Lebedieva, Vira M. Andriievska, and Andrey V. Pikilnyak. Training elementary school teachers-to-be at Computer Science lessons to evaluate e-tools. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3890.

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The study purpose is to develop methodological support for students’ training for evaluation e-tools for young learners and to check its effectiveness experimentally. The module “Expert evaluation of the quality of e-tools for young learners” is offered for teachers-to-be. The determination of the weighting factor of each criterion by expert evaluations was organized. Educational principles, correlation e-tool content with the curriculum, interactivity, multimedia, assistance system, ergonomic requirements are mentioned. On the basis of the criterion rank, the significance of each criterion was calculated. The indicators to determine the level of preliminary expert evaluations of e-tools are proposed. The results are calculated with nonparametric methods of mathematical statistics, in particular, Pearson’s criterion χ2. The conclusion is the expert evaluation has different activity stages, gradually becoming a common phenomenon. Training teachers-to-be for e-tool expert evaluation at Computer Science, Mathematics, English is a complex process.

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Pebay, Philippe Pierre, and Janine Camille Bennett. Parallel auto-correlative statistics with VTK. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1095960.

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