Mastering the R Statistical Package
Preface
Risk Modelling and its Myths
Mastering the R Statistical Package
Key Concepts on Probability
Tools for Describing Risk Factors and Portfolios
The Essentials of Hypothesis Testing for Risk Managers
Alternative Methods to Measure Correlation
A Primer On Maximum Likelihood Estimation
Regression in a Nutshell
Fitting Probability Distributions to Data
Practical Principal Components Analysis
Three Essential Models for Volatility
Random Numbers and Applied Simulation
Tail Risk Modelling
Conclusion to 'Market Risk Modelling (2nd edition)'
R is the statistical toolset of choice for a growing number of quantitative investment professionals, primarily because it offers fast and accurate statistical analysis in a robust and portable environment. You can become competent in R in a matter of hours, as this chapter will show. Since R is free, powerful, extendable and widely used, it offers the ideal tool for elementary and advanced statistical analysis. However, getting started in R can be a frustrating and annoying experience. First, you have to contend with the language of objects, functions and commands. Second, the R reference documentation that accompanies the package is not particularly friendly to non-programming users or those new to statistical methods.
The purpose of this chapter is to ease you gently into using the package by providing hands-on illustrations of R. To gain the most from this chapter, you should work carefully through each illustration. Competence in R requires patience and practice. As you become more familiar with the package, your initial frustration will be transformed into fascination and excitement at the potential of this truly amazing software. Remember this chapter is designed to be
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