After fitting a regression/predictive model, we need to understand what the relative ranking of significant attributes is on a comparative scale. This is explained by Beta parameter estimates. Beta, or standardized coefficients, are the slopes we get if all the variables are on the same scale, which is done by converting them to z-scores before doing the predictive modeling (regression). Beta coefficients allow a comparison of the approximate relative importance of the predictors and hence the variables can be ranked, which neither the unstandardized coefficients nor the Pvalues can. Scaling, or standardizing, the data vectors can be done using the scale() function. Once the scaled variables are created, the regression is redone using them. The resulting coefficients are the beta coefficients.