              The least-squares goodness-of-fit parameter S' for reflection\
               data classified as observed (see _reflns.observed_criterion)\
               after the final cycle of least squares. This parameter explicitly\
               includes the restraints applied in the least-squares process. See\
               also the definition of _refine.ls_goodness_of_fit_obs.\
\
                    (   sum   |w    |Y~obs~  - Y~calc~|^2^| )^1/2^\
                    ( + sum~r~|w~r~ |P~calc~ - P~targ~|^2^| )\
               S' = ( ------------------------------------- )\
                    (      N~ref~ + N~restr~ - N~param~     )\
\
               Y~obs~   = the observed coefficients\
                          (see _refine.ls_structure_factor_coef)\
               Y~calc~  = the observed coefficients\
                          (see _refine.ls_structure_factor_coef)\
               w        = the least-squares reflection weight\
                          [1/(e.s.d. squared)]\
\
               P~calc~  = the calculated restraint values\
               P~targ~  = the target restraint values\
               w~r~     = the restraint weight\
\
               N~refr~  = the number of reflections used in the refinement\
                          (see _refine.ls_number_reflns_obs)\
               N~restr~ = the number of restraints\
                          (see _refine.ls_number_restraints)\
               N~param~ = the number of refined parameters\
                          (see _refine.ls_number_parameters)\
\
               sum  is taken over the specified reflection data\
               sumr is taken over the restraint data