We used least square fitting to calculate the regression coefficients. The most common forms of it include least square fitting of lines and least square fitting of polynomials. In the former, only the first-order term of the predictor variables are involved in the regression model; in the latter, higher order polynomial terms of them are also used. Due to a limited number of observations available (the number of "Generic" and "Markov" datasets in the analysis is about thirty) compared to the number of features, we confined ourselves to the simplest form of linear regression: only the first-order terms are used in the fitting. As we will discuss below, this simplification does not affect the regression result much.