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Books on the topic 'Method of correlation–regression analysis'

Author: Grafiati
Published: 4 June 2025
Last updated: 6 July 2025

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1

Archdeacon, Thomas J. Correlation and regression analysis: A historian's guide. University of Wisconsin Press, 1993.

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2

Archdeacon, Thomas J. Correlation and regression analysis: A historian's guide. University of Wisconsin Press, 1994.

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3

1923-, Cohen Jacob, and Cohen Jacob 1923-, eds. Applied multiple regression/correlation analysis for the behavioral sciences. 3rd ed. L. Erlbaum Associates, 2003.

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4

Sheldon, Zedeck, ed. Data analysis for research designs: Analysis-of-variance and multiple regression/correlation approaches. W.H. Freeman, 1989.

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5

Atkinson, A. C. Plots, transformations and regression: An introduction to graphical methods of diagnostic analysis. Oxford University Press, 1985.

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6

Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Information Science Reference, 2010.

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7

L, Sjoquist David, and Stephan Paula E, eds. Understanding regression analysis: An introductory guide. Sage Publications, 1986.

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8

Keppel, Geoffrey. Data analysis for research designs. Freeman, 1995.

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9

Vogt, W., and R. Johnson. Correlation and Regression Analysis. SAGE Publications Ltd, 2012. http://dx.doi.org/10.4135/9781446286104.

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10

Duan, Naihua. Slicing regression: A link-free regression method. RAND, 1991.

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11

LeBlanc, Michael. An adaptive expansion method for regression. University of Toronto, Department of Statistics, 1993.

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12

Edronova, Valentina. Statistics. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1577822.

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In this textbook, the methodology of statistics is presented in the unity of quantitative assessment and meaningful analysis of the state, development, forecast and connections of socio-economic phenomena. The evolution of statistics as a science and its subject, methods of calculating absolute, relative, average indicators in the assessment of socio-economic phenomena are considered. The content and types of statistical observation and summary are described.
 The characteristic of selective observation and methods of calculating its errors are given. The methods of research of development and forecasting based on the series of dynamics, as well as indices in assessing the development and relationships of phenomena, the basics of correlation and regression analysis of relationships are considered.
 Meets the requirements of the Federal State educational Standard of higher education of the latest generation in the field of training "Economics".
 For students studying in economic specialties.
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13

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

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14

Müller, Hans-Georg. Nonparametric regression analysis of longitudinal data. Springer-Verlag, 1988.

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15

1962-, Fitzmaurice Garrett M., ed. Longitudinal data analysis. Chapman & Hall/CRC, 2008.

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16

Wu, Hulin. Nonparametric regression methods for longitudinal data analysis. Wiley-Interscience, 2006.

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17

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

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18

Press, S. James. Analysis of repeated measures experiments with binary outcomes. Rand, 1987.

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19

Fitzmaurice, Garrett M. Applied longitudinal analysis. 2nd ed. Wiley, 2011.

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20

Seo, Takashi. Testing equality of means and simultaneous confidence intervals in repeated measures with missing data. University of Toronto, Dept. of Statistics, 1997.

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21

Courgeau, Daniel. Analyse démographique des biographies. Editions de l'Institut national d'études démographiques, 1989.

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22

Heitmueller, Axel. A note on decompositions in fixed effects models in the presence of time-invariant characteristics. IZA, 2005.

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23

Racine, Jean. A nonparametric variable kernel method for local adaptive smoothing of regression functions and associated response coefficients. Dept. of Economics, York University, 1991.

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24

J, Brenner Martin, Voelker L. S, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Development of an integrated aeroservoelastic analysis program and correlation with test data. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1991.

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25

Sunteev, Anton. Management of internal reserves to reduce the cost of engineering products. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141766.

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The monograph presents current trends in the management of internal reserves to reduce the cost of production of machine-building enterprises. The approaches of various scientists to the interpretation of the concept of "enterprise reserve" are analyzed. The classification of the types of reserves of the enterprise is given and supplemented by the classification of the resources that form the reserve. Methodological support for the management of internal reserves for reducing the cost of industrial products and methods for identifying them are presented. A technology for studying the cost of production of machine-building enterprises is proposed. The analysis of the state and trends of development of machine-building enterprises of the Russian Federation is carried out and the key problems are identified. The practice of organizing the processes of managing internal reserves to reduce the cost of production at machine-building enterprises is presented. The factors of influence on the change in the cost of production are estimated using factor and correlation-regression analyses. A system for managing internal reserves for reducing the cost of production is formed and the stages and principles of its construction are given. A program of measures to manage internal reserves to reduce the cost of machine-building products and an algorithm for identifying them have been developed.
 For students, researchers and practitioners.
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26

Guttman, Irwin. Bayesian method of detecting change point in regression and growth curve models. University of Toronto, Dept. of Statistics, 1985.

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27

United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Improving the chi-squared approximation for bivariate normal tolerance regions. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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28

Feiveson, Alan H. Improving the chi-squared approximation for bivariate normal tolerance regions. NASA, 1993.

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29

United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Improving the chi-squared approximation for bivariate normal tolerance regions. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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30

Bac-Van, Nguyen. Consistency of least squares estimates in a system of linear correlation models. Polska Akademia Nauk, Instytut Matematyczny, 2001.

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31

Racine, J. S. The semiparametric approach to the estimation of systems of equations models in the presence of heteroskedasticity of unknown form. Dept. of Economics, York University, 1989.

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32

Racine, J. S. Semiparamteric estimation in the presence of heteroskedasticity of unknown form. Dept. of Economics, York University, 1989.

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33

Davis, James B. Statistics using SAS Enterprise Guide. SAS Institute, 2007.

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34

Davis, James B. Statistics using SAS Enterprise Guide. SAS Institute, 2007.

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35

S, Pappa Richard, and Langley Research Center, eds. Finite-element vibration analysis and modal testing of graphite epoxy tubes and correlation between the data. National Aeronautics and Space Administration, Langley Research Center, 1996.

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36

S, Pappa Richard, and Langley Research Center, eds. Finite-element vibration analysis and modal testing of graphite epoxy tubes and correlation between the data. National Aeronautics and Space Administration, Langley Research Center, 1996.

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37

S, Pappa Richard, and Langley Research Center, eds. Finite-element vibration analysis and modal testing of graphite epoxy tubes and correlation between the data. National Aeronautics and Space Administration, Langley Research Center, 1996.

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38

Correlation And Regression Analysis. Sage Publications (CA), 2012.

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39

West, Stephen G., Jacob Cohen, Patricia Cohen, and Leona S. Aiken. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 3rd ed. Lawrence Erlbaum, 2002.

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40

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 2002.

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41

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge, 2013.

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42

West, Stephen G., Jacob Cohen, Patricia Cohen, and Leona S. Aiken. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 2013.

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43

West, Stephen G., Jacob Cohen, Patricia Cohen, and Leona S. Aiken. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 2013.

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44

Cohen, Jacob, and Patricia Cohen. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 1998.

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45

West, Stephen G., Jacob Cohen, Patricia Cohen, and Leona S. Aiken. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Taylor & Francis Group, 2014.

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46

Atkinson, A. C. Plots, Transformations, and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis (Oxford Statistical Science Series). Oxford University Press, USA, 1987.

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47

Atkinson, A. C. Plots, Transformations, and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis (Oxford Statistical Science Series). Oxford University Press, USA, 1986.

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48

Understanding Regression Analysis: An Introductory Guide. SAGE Publications, Incorporated, 2016.

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49

Keppel, Geoffrey. Data Analysis For Research Designs. 2nd ed. Worth Pub, 2008.

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50

Lamkey, Kendall. Quantitative Methods for Plant Breeding. Edited by Walter Suza. Iowa State University Digital Press, 2023. http://dx.doi.org/10.31274/isudp.2023.145.

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This open textbook covers common statistics used in agriculture research, including experimental design in plant breeding and genetics, as well as the analysis of variance, regression, and correlation.
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