Output from program UnitCell - method of TJB Holland & SAT Redfern 1995 sample title: Synthetic spinel refined in cubic system, using wavelength 1.540593 Å minimising the sum of squares of residuals in 2 theta Weighted assuming a value of sigma(2theta) = 0.005 deg Cell parameter errors scale in direct proportion to this weighting value refined ZeroShift 2 theta : -0.1182 ±0.0000 parameter value sigma 95% conf a 5.68762 0.00017 0.00039 cell vol 183.9892 0.0167 0.0378 residuals: standard, average, and maximum deviations:- sd (2T) = 0.0015 aad (2T) =0.0013 maxdev (2T) =0.0025 sigmafit = 0.3233 students t = 2.26 Reciprocal cell parameters: a* params 0.1758204 sigma 0.0000053 Observed and fitted results: {dependent-variable residuals >2sd are bulleted} (2-Theta observations corrected by ZeroShift of -0.1182) no h k l d(obs) d(calc) res(d) 2T.obs 2T.calc res(2T) 1 1 1 1 3.28395 3.28375 0.00020 27.132 27.134 -0.002 2 2 0 0 2.84363 2.84381 -0.00018 31.434 31.432 0.002 3 2 1 0 2.54341 2.54358 -0.00017 35.259 35.256 0.002 4 2 1 1 2.32199 2.32196 0.00003 38.749 38.749 -0.000 5 2 2 0 2.01098 2.01088 0.00010 45.045 45.047 -0.002 6 3 1 1 1.71491 1.71488 0.00002 53.382 53.383 -0.001 7 2 2 2 1.64191 1.64188 0.00004 55.958 55.959 -0.001 8 3 0 2 1.57746 1.57746 -0.00001 58.460 58.460 0.000 9 3 1 2 1.52006 1.52008 -0.00002 60.896 60.895 0.001 10 3 2 1 1.52006 1.52008 -0.00002 60.896 60.895 0.001 Regression diagnostics (for deletion of each observation i): (a) potentially deleterious or influential observations affecting the fit: no h k l hat dfFits Rstudt sigma[i] d(sig)% limit : 0.200 0.632 2.000 (b) observations most strongly affecting the parameter values DfBetas: cell parameter changes (as % of their standard deviations): no h k l da dV