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Books on the topic 'Correlation coefficient'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 September 2023

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1

Keen, K. J. Asymptotic variance of the interclass correlation coefficient. [Toronto, Ont.]: University of Toronto, Department of Statistics, 1989.

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2

Srivastava, M. S. Point and interval estimation of the interclass correlation coefficient. Toronto: University of Toronto, Dept. of Statistics, 1987.

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3

Jackson, Douglas E. Tool for studying the effects of range restriction in correlation coefficient estimation. Brooks Air Force Base, Tex: Air Force Systems Command, Air Force Human Resources Laboratory, 1990.

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4

Sloan, Luke, and Rob Angell. Spearman’s Rank-Order Correlation Coefficient and the General Social Survey (2012): Income and Political Influence. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473937987.

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5

Al-Ahmadi, Adel Bin Musaed Sulaiman. Electrohydrodynamic (EHD) enhancement of condensation heat transfer - development of correlation for heat transfer coefficient for tubular systems. Birmingham: University of Birmingham, 2003.

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6

Sloan, Luke, and Rob Angell. Spearman’s Rank-Order Correlation Coefficient and the General Social Survey (2012): Highest Level of Education and Income. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473937857.

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7

Sloan, Luke, and Rob Angell. Pearson's Correlation Coefficient and the UK Living Cost and Food Survey (2010): The Relationship Between Income and Expenditure. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473937994.

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8

Thomas, Hoben. Distributions of Correlation Coefficients. New York, NY: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4684-6366-8.

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9

Distributions of correlation coefficients. New York: Springer-Verlag, 1989.

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10

Bonilha, M. W. Measurements of correlation coefficients of vibration on a car body shell. Southampton, England: University of Southampton, Institute of Sound and Vibration Research, 1993.

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11

Dufour, Jean-Marie. Distribution-free bounds for serial correlation coefficients in heteroskedastic symmetric time series. Montréal: Centre interuniversitaire de recherche en économie quantitative, 2005.

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12

Dobson, A. P. The correction of correlation coefficients for restriction of range when restriction results from the trunciation of a normally distributed variable. [London]: City University Business School, Centre for Personnel Research and Enterprise Development, 1986.

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13

Dobson, Paul. The correction of correlation coefficients for restriction of range when restriction results from the trunciation of a normally distributed variable. (London): City University Business School, Centre for Personnel Research and Enterprise Development, 1986.

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14

United States. National Aeronautics and Space Administration., ed. Optical correlation of images with signal-dependent noise using constrained-modulation filter devices. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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15

Kalmykov, Sergey, and Nikolay Pashin. Social advertising: designing effective interaction with the target audience. ru: INFRA-M Academic Publishing LLC., 2017. http://dx.doi.org/10.12737/23289.

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The monograph analyzes the possibility of managing the process of socio-advertising influence on socio-demographic groups. The developed methodological bases with the use of the multivariate paradigmatic status of sociological knowledge allowed us to form: principles of designing social advertising interaction, factors of efficiency (quality) of social advertising, a system of sociological quality assurance of social advertising. Insufficiently studied problems of efficiency and quality of social advertising are investigated. The coefficients of the importance of its efficiency factors (quality) are established. Stable interrelations of social advertising influence at the level of revealed correlation coefficients of behavioral reactions of various categories of target audience with their characteristics are proved. The results obtained are summarized in the developed model of behavior of the target audience on the basis of social advertising interaction, and the content modules of the mechanism of controllability of the specified process are proposed.

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16

Dobson, Paul. The correction of correlation coefficients for restriction of range when restriction results from the truncation of a normally distributed variable. London: City University Business School, 1986.

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17

A, Jaberi Farhad, Givi P, and United States. National Aeronautics and Space Administration., eds. LES, DNS, and RANS for the analysis of high-speed turbulent reacting flows: Annual report submitted to NASA Langley Research Center : progress report ... under grant NAG 1-1122 for the period August 1, 1995 - July 31, 1996. [Washington, DC: National Aeronautics and Space Administration, 1996.

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18

Soelberg, Peer. Causal Inference from Cross-Lagged Correlation Coeficients: Fact or Fancy? Creative Media Partners, LLC, 2018.

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19

O'Neal, David J. Effects of variable selection and weighting on the multiple correlation coefficient. 1993.

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20

Pearson's Correlation Coefficient and the Global Health Observatory Data (2012): Disaster Preparedness and Food Safety. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473948167.

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21

Pearson’s Correlation Coefficient and the Early Childhood Longitudinal Study (1998): Reading and Math Performance in Kindergarten. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473942950.

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22

Pearson’s Correlation Coefficient and the U.S. Statistical Abstracts (2012): Poverty and Infant Mortality Across the U.S. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473947405.

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23

Davis, Lori Ann. estimating random effects and serial correlation in random coefficient models: To weight or not to weight. 2002.

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24

Pearson's Correlation Coefficient and the Integrated Postsecondary Education Data System (2012): Graduation Rates by Type of College. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473937901.

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25

Tables of the Ordinates and Probability Integral of the Distribution of the Correlation Coefficient in Small Samples. Cambridge University Press, 2009.

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26

Spearman's Rank-Order Correlation Coefficient and the World DataBank (2012): Gross Domestic Product Per Capita and Internet Access. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473937871.

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27

Pearson’s Correlation Coefficient and the Consolidated State Performance Report (2012–2013): High School Science Proficiency across the U.S. States. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473960596.

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28

Hempel, Susanne. Reliability. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0015.

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This chapter discusses reliability. It outlines the nature and purpose of reliability, classical test theory, measures of reliability (measure orientated reliability, parallel test, and test-retest) as well as internal consistency, inter-item correlation, coefficient alpha, and categorical judgements.

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29

Spearman’s Rank-Order Correlation Coefficient and the Global Health Observatory Data (2012): Life Expectancy and Per Capita Government Health Spending. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2015. http://dx.doi.org/10.4135/9781473948181.

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30

Guimarães, Ms Celso Cursino. The trap of Pearson's product-moment correlation: How the instability and mathematical indetermination of this coefficient have made work inaccurate over decades. http://cbl.org.br/, 2020.

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31

Thomas, Hoben. Distributions of Correlation Coefficients. Springer London, Limited, 2012.

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32

Distributions of Correlation Coefficients. Springer, 2012.

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33

Henriksen, Niels Engholm, and Flemming Yssing Hansen. Rate Constants, Reactive Flux. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805014.003.0005.

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This chapter discusses a direct approach to the calculation of the rate constant k(T) that bypasses the detailed state-to-state reaction cross-sections. The method is based on the calculation of the reactive flux across a dividing surface on the potential energy surface. Versions based on classical as well as quantum mechanics are described. The classical version and its relation to Wigner’s variational theorem and recrossings of the dividing surface is discussed. Neglecting recrossings, an approximate result based on the calculation of the classical one-way flux from reactants to products is considered. Recrossings can subsequently be included via a transmission coefficient. An alternative exact expression is formulated based on a canonical average of the flux time-correlation function. It concludes with the quantum mechanical definition of the flux operator and the derivation of a relation between the rate constant and a flux correlation function.

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34

Yoo, Mira S. Testing equality of dependent correlation coefficients. 1994.

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35

Streiner, David L., Geoffrey R. Norman, and John Cairney. Reliability. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199685219.003.0008.

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This chapter reviews the basic theory of reliability, and examines the relation between reliability and measurement error. It derives the standard form of reliability, the intraclass correlation or ICC, from repeated measures ANOVA. The chapter explores issues in the application of the reliability coefficient, including absolute versus relative reliability, the reliability of multiple observations, and the standard error of measurement. It examines several other measures of reliability—Cohen’s kappa, Pearson r, and the method of Altman and Bland—and derives the relation between them and the ICC. The chapter determines the variance of a reliability estimate. It also calculates sample size estimates for reliability studies, and methods to combine reliability estimates in systematic reviews.

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36

Allen, Michael P., and Dominic J. Tildesley. How to analyse the results. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0008.

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In this chapter, practical guidance is given on the calculation of thermodynamic, structural, and dynamical quantities from simulation trajectories. Program examples are provided to illustrate the calculation of the radial distribution function and a time correlation function using the direct and fast Fourier transform methods. There is a detailed discussion of the calculation of statistical errors through the statistical inefficiency. The estimation of the error in equilibrium averages, fluctuations and in time correlation functions is discussed. The correction of thermodynamic averages to neighbouring state points is described along with the extension and extrapolation of the radial distribution function. The calculation of transport coefficients by the integration of the time correlation function and through the Einstein relation is discussed.

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37

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Definitions and concepts. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0002.

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A network is specified by its links and nodes. However, it can be described by a much wider range of interesting and important topological features. This chapter introduces how a network can be characterized by its microscopic topological features and macroscopic topological features. Microscopic features introduced are degree and clustering coefficients. Macroscopic topological features introduced are the degree distribution; correlation between degrees of connected nodes; modularity; and, the eigenvalue spectrum (which counts the number of closed paths in the graph).

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38

Read, E. J. C., J. C. Willmott, and G. Roland Kaye. Tables of Coefficients for the Analysis of Triple Angular Correlations of Gamma-Rays from Aligned Nuclei. Elsevier Science & Technology Books, 2013.

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39

Ross, John, Igor Schreiber, and Marcel O. Vlad. Determination of Complex Reaction Mechanisms. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780195178685.001.0001.

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In a chemical system with many chemical species several questions can be asked: what species react with other species: in what temporal order: and with what results? These questions have been asked for over one hundred years about simple and complex chemical systems, and the answers constitute the macroscopic reaction mechanism. In Determination of Complex Reaction Mechanisms authors John Ross, Igor Schreiber, and Marcel Vlad present several systematic approaches for obtaining information on the causal connectivity of chemical species, on correlations of chemical species, on the reaction pathway, and on the reaction mechanism. Basic pulse theory is demonstrated and tested in an experiment on glycolysis. In a second approach, measurements on time series of concentrations are used to construct correlation functions and a theory is developed which shows that from these functions information may be inferred on the reaction pathway, the reaction mechanism, and the centers of control in that mechanism. A third approach is based on application of genetic algorithm methods to the study of the evolutionary development of a reaction mechanism, to the attainment given goals in a mechanism, and to the determination of a reaction mechanism and rate coefficients by comparison with experiment. Responses of non-linear systems to pulses or other perturbations are analyzed, and mechanisms of oscillatory reactions are presented in detail. The concluding chapters give an introduction to bioinformatics and statistical methods for determining reaction mechanisms.

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40

Correlation of Supersonic Convective Heat-Transfer Coefficients from Measurements of the Skin Temperature of a Parabolic Body of Revolution (NACA RM-10). Creative Media Partners, LLC, 2021.

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41

Newman, Mark. The configuration model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0012.

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A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

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