The conventional error analysis of linear least squares problems goes as follows . As above, let be the solution to minimizing computed by LAPACK using one of the least squares drivers xGELS, xGELSS or xGELSX (see subsection 2.2.2). We discuss the most common case, where A is overdetermined (i.e., has more rows than columns) and has full rank :
The computed solution has a small normwise backward error. In other words minimizes , where E and f satisfy
and p(n) is a modestly growing function of n. We take p(n) = 1 in the code fragments above. Let (approximated by 1/RCOND in the above code fragments), (= RNORM above), and (SINT = RNORM / BNORM above). Here, is the acute angle between the vectors and . Then when is small, the error is bounded by
where = COST and = TANT in the code fragments above.
We avoid overflow by making sure RCOND and COST are both at least EPSMCH, and by handling the case of a zero B matrix separately (BNORM = 0).
may be computed directly from the singular values of A returned by xGELSS (as in the code fragment) or by xGESVD. It may also be approximated by using xTRCON following calls to xGELS or xGELSX. xTRCON estimates or instead of , but these can differ from by at most a factor of n.
If A is rank-deficient, xGELSS and xGELSX can be used to regularize the problem by treating all singular values less than a user-specified threshold () as exactly zero. The number of singular values treated as nonzero is returned in RANK. See  for error bounds in this case, as well as  for the underdetermined case.
The solution of the overdetermined, full-rank problem may also be characterized as the solution of the linear system of equations
By solving this linear system using xyyRFS or xyySVX (see section 4.4) componentwise error bounds can also be obtained .