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A Primer in Applied Regression Analysis

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

Abstract

Regression modeling lies at the heart of modern statistical analysis. It also occupies a key role in much of the analysis carried out in quantitative finance. Given its importance and frequency of use, this chapter provides a hands-on introduction to applied regression modeling. The emphasis is on using R to analyze some simple data sets contained in the R package. The objective is to give you a feel for regression techniques using simple (nonenergy) examples before we move onto discuss more energy-specific applications of regression in the remaining chapters of this book. The emphasis of this chapter is therefore on you the reader becoming comfortable with the ideas surrounding regression and replicating for yourself the examples given in R.

Keywords

Ordinary Little Square Slope Parameter Simple Linear Regression Model Ordinary Little Square Estimator Forward Contract 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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

There are no affiliations available

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