A Primer in Applied Regression Analysis
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.
KeywordsCovariance Liner Kerosene Hedging
Unable to display preview. Download preview PDF.
- Doran, H. E. (1989) Applied Regression Analysis in Econometrics, Marcel Dekker, Inc., New York.Google Scholar
- 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
- 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
- Weisberg, S. (1985) Applied Linear Regression, John Wiley and Sons, New York.Google Scholar