Multiple Regression and Prediction

  • Nigel Da Costa Lewis
Part of the Finance and Capital Markets Series book series (FCMS)


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.


Linear Regression Model Multiple Regression Model Multiple Linear Regression Model Prediction Interval Simple Linear Regression Model 
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Further Reading

  1. Doran, H. E. (1989) Applied Regression Analysis in Econometrics, Marcel Dekker, Inc., New York.Google Scholar
  2. Lewis, Nigel Da Costa (2004) Operational Risk with Excel and VBA: Applied Statistical Methods for Risk Management, John Wiley & Sons, Inc., New York.Google Scholar
  3. Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996) Applied Linear Regression Models (3rd edn), Richard D. Irwin, Inc., Chicago, IL.Google Scholar
  4. Weisberg, S. (1985) Applied Linear Regression, John Wiley and Sons, New York.Google Scholar

Copyright information

© Nigel Da Costa Lewis 2005

Authors and Affiliations

  • Nigel Da Costa Lewis

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