Abstract
In this chapter we extend the simple linear regression model into the multiple linear regression model. Multiple regression is useful when we can expect more than one independent variable to influence the dependent variable. It allows us to explore the relationship between several independent and a single dependent variable. We also discuss multivariate regression which arises when we have several dependent variables dependent on the same (or some subset) independent variables.
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Further Reading
Doran, H. E. (1989) Applied Regression Analysis in Econometrics, Marcel Dekker, Inc., New York.
Lewis, Nigel Da Costa (2004) Operational Risk with Excel and VBA: Applied Statistical Methods for Risk Management, John Wiley & Sons, Inc., New York.
Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996) Applied Linear Regression Models (3rd edn), Richard D. Irwin, Inc., Chicago, IL.
Weisberg, S. (1985) Applied Linear Regression, John Wiley and Sons, New York.
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© 2005 Nigel Da Costa Lewis
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Da Costa Lewis, N. (2005). Multiple Regression and Prediction. In: Energy Risk Modeling. Finance and Capital Markets Series. Palgrave Macmillan, London. https://doi.org/10.1057/9780230523784_9
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DOI: https://doi.org/10.1057/9780230523784_9
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-51702-2
Online ISBN: 978-0-230-52378-4
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