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Books on the topic 'Analysis correlation-regression'

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Published: 30 May 2022

Last updated: 31 May 2022

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1

Vogt, W., and R. Johnson. Correlation and Regression Analysis. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2012. http://dx.doi.org/10.4135/9781446286104.

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2

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

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3

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

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4

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

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5

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

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6

Analyzing environmental data. Hoboken, NJ: Wiley, 2004.

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7

Plots, transformations, and regression: An introduction to graphical methods of diagnostic regression analysis. Oxford: Clarendon Press, 1985.

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8

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

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9

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

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10

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

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11

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

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12

Davis, James B. Statistics using SAS Enterprise Guide. Cary, N.C: SAS Institute, 2007.

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13

Davis, James B. Statistics using SAS Enterprise Guide. Cary, N.C: SAS Institute, 2007.

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14

Sunteev, Anton. Management of internal reserves to reduce the cost of engineering products. ru: 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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15

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

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16

Applying Regression and Correlation: A Guide for Students and Researchers. Sage Publications Ltd, 2000.

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17

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

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18

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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19

Cohen, Patricia. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Psychology Press, 2014. http://dx.doi.org/10.4324/9781410606266.

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20

Cohen. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge, 2013. http://dx.doi.org/10.4324/9780203774441.

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21

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

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22

Applying Regression and Correlation: A Guide for Students and Researchers. Sage Publications Ltd, 2001.

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23

Bailer, A. John, and Walter W. Piegorsch. Analyzing Environmental Data. Wiley & Sons, Incorporated, John, 2005.

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24

Bailer, A. John, and Walter W. Piegorsch. Analyzing Environmental Data. Wiley & Sons, Incorporated, John, 2008.

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25

Bailer, A. John, and Walter W. Piegorsch. Analyzing Environmental Data. Wiley, 2005.

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26

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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27

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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28

Robertson, Rob. Effects of collinearity, sample size, multiple correlation, and predictor-criterion correlation salience on the order of variable entry in stepwise regression. 1997.

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29

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

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30

Statistics Using SAS Enterprise Guide (SAS Press) (SAS Press). SAS Publishing, 2007.

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31

Li, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.

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This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R in research projects. Specifically, it teaches students how to import, inspect, and manage data; understand the logic of statistical inference; visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots; and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. Third, it teaches students how to replicate the findings in published journal articles and diagnose model assumption violations. The principle behind this book is to teach students to learn as little R as possible but to do as much reproducible, substance-driven data analysis at the beginner or intermediate level as possible. The minimalist approach dramatically reduces the learning cost but still proves adequate information for meeting the practical research needs of senior undergraduate and beginning graduate students. Having completed this book, students can use R and statistical analysis to answer questions regarding some substantively interesting continuous outcome variable in a cross-sectional design.

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32

Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Analysing relationships between variables. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.003.0009.

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Chapter 9 discusses how to analyse relationships between variables, including how to use, interpret, and report Pearson’s correlation, rank correlation, and regression. It discusses the use of transformation in regression and how to report results. The chapter includes analyses using Stata, SAS, SPSS, and R.

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33

Peacock, Janet L., and Sally M. Kerry. Analysing relationships between variables. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780198599661.003.0009.

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34

Halperin, Sandra, and Oliver Heath. 16. Patterns of Association. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0016.

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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particular emphasis on hypothesis testing and significance tests. It discusses ordinary least squares (OLS) regression and cross-tabulation, two of the most widely used statistical analysis techniques in political research. Finally, it explains how to state the null hypothesis, calculate the chi square, and establishing the correlation between the dependent and independent variables.

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35

Ferreira, Eliel Alves, and João Vicente Zamperion. Excel: Uma ferramenta estatística. Brazil Publishing, 2021. http://dx.doi.org/10.31012/978-65-5861-400-5.

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This study aims to present the concepts and methods of statistical analysis using the Excel software, in a simple way aiming at a greater ease of understanding of students, both undergraduate and graduate, from different areas of knowledge. In Excel, mainly Data Analysis Tools will be used. For a better understanding, there are, in this book, many practical examples applying these tools and their interpretations, which are of paramount importance. In the first chapter, it deals with introductory concepts, such as introduction to Excel, the importance of statistics, concepts and definitions. Being that in this will be addressed the subjects of population and sample, types of data and their levels of measurement. Then it brings a detailed study of Descriptive Statistics, where it will be studied percentage, construction of graphs, frequency distribution, measures of central tendency and measures of dispersion. In the third chapter, notions of probability, binomial and normal probability distribution will be studied. In the last chapter, Inferential Statistics will be approached, starting with the confidence interval, going through the hypothesis tests (F, Z and t tests), ending with the statistical study of the correlation between variables and simple linear regression. It is worth mentioning that the statistical knowledge covered in this book can be useful for, in addition to students, professionals who want to improve their knowledge in statistics using Excel.

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