Gets the effective rank of the matrix A.

Gets the vector of residuals. If y is the right-hand side of the least squares equation Ax = y, and we denote by yhat the vector Ax where x is the computed least squares solution, then the vector of residuals r is the vector whose ith component is r[i] = y[i] - yhat[i].

Gets the residual sum of squares. If y is the right-hand side of the least squares equation Ax = y, and we denote by yhat the vector Ax where x is the computed least squares solution, then the residual sum of squares is defined to be (y[0] - yhat[0])^2 + (y[1] - yhat[1])^2 + ... + (y[m-1] - yhat[m-1])^2.

Gets the tolerance used to compute the effective rank of the input matrix A.

Gets the least squares solution x for the least squares problem Ax = y.

Gets the predicted value of y by computing yHat = Ax, where x is the calculated solution to the least squares problem Ax = y.