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Books on the topic 'Multiply regression'

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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1

Keith, Timothy Z. Multiple Regression and Beyond. Routledge, 2019. http://dx.doi.org/10.4324/9781315162348.

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2

Berry, William, and Stanley Feldman. Multiple Regression in Practice. SAGE Publications, Inc., 1985. http://dx.doi.org/10.4135/9781412985208.

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3

Stanley, Feldman, ed. Multiple regression in practice. Sage Publications, 1985.

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4

Fox, John. Multiple and Generalized Nonparametric Regression. SAGE Publications, Inc., 2000. http://dx.doi.org/10.4135/9781412985154.

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5

Jaccard, James, and Robert Turrisi. Interaction Effects in Multiple Regression. SAGE Publications, Inc., 2003. http://dx.doi.org/10.4135/9781412984522.

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6

Jaccard, James. Interaction effects in multiple regression. 2nd ed. Sage Publications, 2003.

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7

Robert, Turrisi, and Wan Choi K, eds. Interaction effects in multiple regression. Sage Publications, 1990.

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8

Krebs-Brown, Axel Johannes. Shrinkage and calibration in multiple regression. typescript, 2000.

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9

G, West Stephen, and Reno Raymond R, eds. Multiple regression: Testing and interpreting interactions. Sage Publications, 1991.

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10

Orme, John G. Multiple regression with discrete dependent variables. Oxford University Press, 2009.

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11

Kukreja, Jyoti. The Single Simplified Guide to Multiple Regression. SAGE Publications Ltd, 2023. http://dx.doi.org/10.4135/9781529670363.

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12

Jaccard, James, and Choi Wan. LISREL Approaches to Interaction Effects in Multiple Regression. SAGE Publications, Inc., 1996. http://dx.doi.org/10.4135/9781412984782.

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13

University, Open, ed. Quantitative methods in business: Unit 13 : Multiple regression. Open University, 2003.

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14

E, Herbert Donald, Myers Raymond H, and American Association of Physicists in Medicine., eds. Multiple regression analysis: Applications in the health sciences. Published for the American Association of Physicists in Medicine by the American Institute of Physics, 1986.

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15

K, Wan Choi, ed. LISREL approaches to interaction effects in multiple regression. Sage Publications, 1996.

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16

Simpson, William Turner. Estimating air drying times of lumber with multiple regression. U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory (One Gifford Pinchot Dr., Madison, 53726-2398), 2004.

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17

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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18

Hrdle, Wolfgang. Investigating smooth multiple regression by the method of average derivatives. Sloan School of Management, Massachusetts Institute of Technology, 1988.

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19

Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0.

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20

Shelton, Katherine Lesley. An illustration of heteroscedasticity in the multiple linear regression model. The author], 1985.

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21

J, Niccolucci Michael, Schuster Ervin G, and Intermountain Research Station (Ogden, Utah), eds. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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22

Yanez, Karina Gabriela Diaz. Multiple Imputation for Handling Missing Data of Covariates in Meta-Regression. [publisher not identified], 2021.

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23

Otsu, N. Multiple regression analysis approach to automatic design of adaptive image processing systems. National Research Council of Canada, Division of Electrical Engineering, 1987.

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24

Gelman, Andrew, and Deborah Nolan. Multiple regression and nonlinear models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0010.

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Abstract:

This chapter covers multiple regression and links statistical inference to general topics such as lurking variables that arose earlier. Many examples can be used to illustrate multiple regression, but we have found it useful to come to class prepared with a specific example, with computer output (since our students learn to run the regressions on the computer). We have found it is a good strategy to simply use a regression analysis from some published source (e.g., a social science journal) and go through the model and its interpretation with the class, asking students how the regression results would have to differ in order for the study’s conclusions to change. The chapter includes examples that revisit the simple linear model of height and income, involve the class in models of exam scores, and fit a nonlinear model (for more advanced classes) for golf putting.

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25

West, Stephen G., Leona S. Aiken, Heining Cham, and Yu Liu. Multiple Regression. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199793549.013.0013.

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26

Caldwell, Sally. Multiple Regression Unplugged. Taylor & Francis Group, 2010.

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27

Multiple Linear Regression. Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630138.

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28

Hj Jubok, Zainodin, Khuneswari Gopal Pillay, and Noraini Abdullah. Model Building Approach in Multiple Regression. UMS Press, 2018. http://dx.doi.org/10.51200/modelbuildingumspress2018-978-967-2166-14-6.

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Abstract:

This book is the outcome of our three years of research work. The problems faced by the undergraduate students in analysing the data on multiple regressions and the importance of this method had inspired the authors to come up with this book. Regression analysis is commonly used by most researchers in business, social and behavioural sciences, biological sciences and many other fields. But there is no proper procedure or approach of model-building in regression analysis. Therefore, this book is aimed at illustrating the procedures to find the best model and the model-building approach. The model-building approach is important in obtaining the best model that well describes the corresponding data set. This approach can be used in various research fields such as in economics, environmental, biological and medical sciences. The multiple regression model-building approach is applicable in various fields of research. It is very useful for researchers to obtain the best model that well describes the data. The approach is also useful in identifying the factors that will affect the dependent variable. The overall thrusts of the authors’ efforts have been geared in explaining explicitly the statistical method using regression analysis, the model building procedures and its applications, so as to meet the needs of today’s students. A substantial effort has gone in addressing multicollinearity issues and illustrating steps to overcome them. The authors hope that readers will find them helpful.

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29

Roback, Paul, and Julie Legler. Beyond Multiple Linear Regression. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429066665.

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30

Keith, Timothy Z. Multiple Regression and Beyond. Routledge, 2014. http://dx.doi.org/10.4324/9781315749099.

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31

Multiple regression and beyond. Allyn and Bacon, 2005.

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32

Multiple Regression And Beyond. Routledge, 2019.

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33

Multiple regression: A primer. Pine Forge Press, 1999.

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34

Multiple Regression in Practice. 1985.

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35

Richardson, Ronny. Business Applications of Multiple Regression. Business Expert Press, 2015.

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36

Multiple Regression and Causal Analysis. Waveland Pr Inc, 2002.

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37

Multiple Regression: A Practical Introduction. SAGE Publications, Incorporated, 2021.

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38

Business Applications of Multiple Regression. Business Expert Press, 2011.

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39

Pedhazur. Multiple Regression in Behavioral Research. 3rd ed. AIPI, 2006.

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40

Richardson, Ronny. Business Applications of Multiple Regression. Business Expert Press, 2011.

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41

Richardson, Ronny. Business Applications of Multiple Regression. Business Expert Press, 2011.

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42

Richardson, Ronny. Business Applications of Multiple Regression. Business Expert Press, 2015.

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43

Creating Multiple Linear Regression Model. Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630251.

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44

Turrisi, Rob. Interaction Effects in Multiple Regression. 2003.

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45

Multiple regression and causal analysis. F.E. Peacock Publishers, 1994.

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46

Multiple and Generalized Nonparametric Regression. 2000.

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47

Keith, Timothy Z. Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling. Taylor & Francis Group, 2014.

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48

Keith, Timothy. Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling. Taylor & Francis Group, 2014.

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49

Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling. Routledge, 2014.

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50

Keith, Timothy Z. Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling. Taylor & Francis Group, 2019.

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