Given a data matrix D with d=5 features/columns with a total variance of 100, an analyst performs a PCA via eigenvalue decomposition, with the resulting eigenvalues as [35,25,20,15,5]. If the analyst wishes to reduce dimensionality with 80% of variance explained, how many dimensions would the analyst be able to reduce their selection to? What would be the standard deviations _i of the data for each these selected dimensions?