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Books on the topic 'Linear regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Groß, Jürgen. Linear Regression. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55864-1.

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Olive, David J. Linear Regression. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1.

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3

Weisberg, Sanford. Applied Linear Regression. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471704091.

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4

1946-, Lee A. J., ed. Linear regression analysis. 2nd ed. Hoboken, N.J: Wiley-Interscience, 2003.

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5

Seber, George A. F. Linear regression analysis. 2nd ed. Hoboken, NJ: Wiley-Interscience, 2002.

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6

Applied linear regression. 3rd ed. Hoboken, N.J: Wiley-Interscience, 2005.

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7

Applied linear regression. 2nd ed. New York: Wiley, 1985.

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8

Weisberg, Sanford. Applied Linear Regression. New York: John Wiley & Sons, Ltd., 2005.

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9

John, Neter, ed. Applied linear regression models. 3rd ed. Chicago: Irwin, 1996.

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10

Regression and linear models. New York: McGraw-Hill, 1990.

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11

Andersen, Per Kragh, and Lene Theil Skovgaard. Regression with Linear Predictors. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7170-8.

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12

Chris, Nachtsheim, and Neter John, eds. Applied linear regression models. 4th ed. Boston: McGraw-Hill/Irwin, 2004.

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13

William, Wasserman, and Kutner Michael H, eds. Applied linear regression models. 2nd ed. Homewood, Ill: Irwin, 1989.

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14

Theil, Skovgaard Lene, and Graversen Therese, eds. Regression with linear predictors. New York: Springer, 2010.

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15

Kutner, Michael H. Applied linear regression models. 4th ed. New York, NY: McGraw-Hill, 2003.

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16

1953-, Peck Elizabeth A., and Vining G. Geoffrey 1954-, eds. Introduction to linear regression analysis. 5th ed. Hoboken, NJ: Wiley, 2012.

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17

Chatterjee, Samprit. Sensitivity analysis in linear regression. New York: Wiley, 1988.

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18

Chatterjee, Samprit, and Ali S. Hadi, eds. Sensitivity Analysis in Linear Regression. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1988. http://dx.doi.org/10.1002/9780470316764.

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19

Fry, John M. (John Michael), 1980-, ed. Regression: Linear models in statistics. London: Springer, 2010.

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20

1953-, Peck Elizabeth A., ed. Introduction to linear regression analysis. 2nd ed. New York: Wiley, 1992.

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21

1953-, Peck Elizabeth A., and Vining G. Geoffrey 1954-, eds. Introduction to linear regression analysis. 4th ed. Hoboken, NJ: Wiley-Interscience, 2006.

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22

1953-, Peck Elizabeth A., and Vining G. Geoffrey 1954-, eds. Introduction to linear regression analysis. 3rd ed. New York: Wiley, 2001.

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23

Mathematical algorithms for linear regression. Boston: Academic Press, 1991.

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24

Dasgupta, Hirak. How to Use Linear Regression. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Inc., 2023. http://dx.doi.org/10.4135/9781529669435.

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25

Seber, G. A. F. Nonlinear regression. Hoboken, N.J: Wiley-Interscience, 2003.

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26

1952-, Wild C. J., ed. Nonlinear regression. New York: Wiley, 1989.

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27

Sue, Younger Mary, ed. A first course in linear regression. 2nd ed. Boston, MA: Duxbury Press, 1985.

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28

Krämer, Walter, and Harald Sonnberger. The Linear Regression Model Under Test. Heidelberg: Physica-Verlag HD, 1986. http://dx.doi.org/10.1007/978-3-642-95876-2.

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29

Green, Peter J., and Bernard W. Silverman. Nonparametric Regression and Generalized Linear Models. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-4473-3.

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30

1974-, Su Xiaogang, ed. Linear regression analysis: Theory and computing. Hackensack, N.J: World Scientific, 2009.

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31

Ronald, Christensen. Log-linear models and logistic regression. 2nd ed. New York: Springer, 1997.

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32

Hoffmann, John P. Linear Regression Analysis: Assumptions and Applications. Washington, USA: NASW Press, National Association of Social Workers, 2015.

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33

Olive, David J. Linear Regression. Springer, 2017.

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34

Groß, Jürgen. Linear Regression. Springer London, Limited, 2012.

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35

Olive, David J. Linear Regression. Springer International Publishing AG, 2018.

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36

Linear Regression. Springer, 2003.

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37

Gelman, Andrew, and Deborah Nolan. Linear regression and correlation. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0005.

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This chapter addresses the descriptive treatment of linear regression with a single predictor: straight-line fitting, interpretation of the regression line and standard deviation, the confusing phenomenon of “regression to the mean,” correlation, and conducting regressions on the computer. These concepts are illustrated with student discussions and activities. Many examples are of the sort commonly found in statistics textbooks, but the focus here is on how to work the examples into student-participation activities rather than simply examples to be read or shown on the blackboard. Topics include the following relationships: height and income, height and hand span, world population over time, and exam scores.

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38

Linear Regression Diagnostics. Franklin Classics, 2018.

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39

Kuh, Edwin, Roy E. Welsch, and NBER Computer Research Center. Linear Regression Diagnostics. Franklin Classics, 2018.

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40

Weisberg, Sanford. Applied Linear Regression. Wiley & Sons, Incorporated, John, 2021.

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41

Weisberg, Sanford. Applied Linear Regression. Wiley & Sons, Incorporated, John, 2013.

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42

Cardot, Hervé, and Pascal Sarda. Functional Linear Regression. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.

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Abstract:

This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.

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43

Lee, Alan J., and George A. F. Seber. Linear Regression Analysis. Wiley & Sons, Incorporated, John, 2012.

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44

Applied Linear Regression. Wiley & Sons, Incorporated, John, 2014.

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45

Lee, Alan J., and George A. F. Seber. Linear Regression Analysis. Wiley & Sons, Incorporated, John, 2012.

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46

Seber, George A., and Alan J. Lee. Linear Regression Analysis. Wiley & Sons, Incorporated, John, 2012.

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47

Weisberg, Sanford. Applied Linear Regression. Wiley & Sons, Incorporated, John, 2021.

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48

Weisberg, Sanford. Applied Linear Regression. Wiley & Sons, Incorporated, John, 2021.

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49

Applied Linear Regression. Wiley, 2013.

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50

Kuh, Edwin, Roy E. Welsch, and Nber Computer Research Center. Linear Regression Diagnostics. Franklin Classics Trade Press, 2018.

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