# Table 2 Prediction equation for G1 and G2 subjects

G1 subjects (n = 277)
Measured FFMDXA 50.54 ± 11.58 kg
Prediction FFMBIA 12.518 + 0.215 w + 0.397 h2/ ZF-F – 0.143 y + 7.843 S (Female = 0, Male = 1), (r2 = 0.92, SE = 3.23 kg, CV = 6.3 %) (1.a)
Prediction FFM
Cross-validation using G2 subjects FFM 50.48 ± 10.97 kg, r = 0.96, bias ± SD = −0.06 ± 3.22 kg, PE = 3.22 kg, RMSE = 2.31 kg, LOA = −6.46 to 6.38 kg
G2 subjects (n = 277)
Measured FFMDXA 49.81 ± 10.91 kg
Prediction FFMBIA 13.639 + 0.192 w + 0.392 h2/ ZF-F – 0.129 y + 8.355 S (Female = 0, Male = 1), (r2 = 0.92, SEE = 3.13 kg, CV = 6.1 %) (1.b)
Prediction FFM
Cross-validation using G1 subjects FFM 49.86 ± 10.60 kg, r = 0.96, bias ± SD = 0.05 ± 3.13 kg, PE = 3.12 kg, RMSE = 2.18 kg, LOA = −6.21 to 6.31 kg
1. FFM, fat free mass; Regression coefficient estimate ± SE; FFMDXA, DXA measurement of FFM; FFMBIA, BIA prediction of FFM; h2/ZF-F, height 2/impedance; SEE, standard error of estimate; LOA, limits of agreement
2. RMSE, Root mean square error= $$\sqrt{{\displaystyle \sum}\frac{{\left({\mathrm{y}}_{\mathrm{i}}^{\hbox{'}}-{\mathrm{y}}_{\mathrm{i}}\right)}^2}{\mathrm{n}}-\mathrm{p}-1}$$, where y’ the predicted FFM, y is the observed; n is the number of subjects, and p is the number of predictor variables; PE, Pure errors= $$\sqrt{{\displaystyle \sum}\frac{{\left({\mathrm{y}}_{\mathrm{i}}^{\hbox{'}}-{\mathrm{y}}_{\mathrm{i}}\right)}^2}{\mathrm{n}}}$$; r, correlation coefficient between FFMBIA and FFMDXA