Abstract
Correlation is a tool for understanding the relationship between two quantities. Regression considers how one quantity is influenced by another. In correlation analysis the two quantities are considered symmetrically: in regression analysis one is supposed dependent on the other, in an unsymmetric way. Extensions to sets of quantities are important.
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Bibliography
Efron, B., and C. Morris. 1975. Data analysis using Stein’s estimator and its generalizations. Journal of the American Statistical Association 70: 311–319.
Hoerl, A.E., and R.W. Kennard. 1970. Ridge regression: Biased estimation of non-orthogonal problems. Technometrics 12: 55–67.
Seber, G.A.F. 1977. Linear regression analysis. New York: Wiley.
Vinod, H.D., and A. Ullah. 1981. Recent advances in regression methods. New York: Dekker.
Zellner, A. 1971. An Introduction to Bayesian inference in econometrics. New York: Wiley.
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Lindley, D.V. (2018). Regression and Correlation Analysis. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_1873
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DOI: https://doi.org/10.1057/978-1-349-95189-5_1873
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Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-95188-8
Online ISBN: 978-1-349-95189-5
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