Summary
In this chapter, we have discussed the most commonly used distributions in the finance domain and associated metrics computations in R; sampling (random and stratified); measures of central tendencies; correlations and types of correlation used for model selections in time series; hypothesis testing (one-tailed/two-tailed) with known and unknown variance; detection of outliers; parameter estimation; and standardization/normalization of attributes in R to bring attributes on comparable scales.
In the next chapter, analysis done in R associated with simple linear regression, multivariate linear regression, ANOVA, feature selection, ranking of variables, wavelet analysis, fast Fourier transformation, and Hilbert transformation will be covered.
