Table 5. Chosen results of the best subset multiple regression methodology. VRmax VRcont Sample R2 Xi p β Sample R2 Xi p β FH1 0.794 FH1 0.763 VcontPTilt 0.000 -0.245 VmaxRKFE 0.000 -0.373 VcontRShIE 0.000 -0.535 VmaxRHIE 0.000 -0.296 VmaxRShAA 0.000 0.376 VmaxPObli 0.000 0.293 VminRHFE 0.000 -0.434 VminSObli 0.000 -0.553 VminRAFE 0.000 0.486 VminRHFE 0.000 -0.622 FH2 0.803 FH2 0.818 VcontRHAA 0.000 -0.296 VcontRHAA 0.000 -0.291 VcontLShIE 0.000 0.851 VcontLShIE 0.000 0.800 VcontRShAA 0.000 -0.449 VcontRShAA 0.000 -0.390 VcontRShIE 0.000 -1.372 VcontRShIE 0.000 -1.398 VminLShFE 0.000 -1.003 VminLShFE 0.000 -1.088 FH3 0.905 FH3 0.892 VcontLShAA 0.000 -0.274 VcontLShAA 0.000 -0.266 VcontRShIE 0.000 -0.816 VcontRShIE 0.000 -0.781 VmaxLHFE 0.000 -0.312 VmaxLHFE 0.000 -0.284 VmaxRAFE 0.000 -0.234 VmaxRAFE 0.000 -0.240 VminRHFE 0.000 -0.498 VminRHFE 0.000 -0.511 BH1 0.833 BH1 0.755 VcontRWIE 0.000 0.417 VcontSRot 0.000 -0.447 VcontSRot 0.000 -0.540 VcontRShFE 0.000 -0.261 VmaxRShAA 0.000 0.584 VcontRShAA 0.000 0.440 VmaxLEFE 0.001 -0.152 VmaxRShAA 0.000 0.817 VminLHFE 0.000 -0.308 VminLShAA 0.002 0.203 BH2 0.666 BH2 0.465 VcontREFE 0.000 0.295 VcontREIE 0.001 -0.253 VmaxLHAA 0.000 0.599 VmaxLHAA 0.000 0.457 VmaxPTilt 0.000 -0.347 VmaxLKFE 0.001 -0.277 VmaxRShAA 0.000 0.701 VmaxRShAA 0.000 0.486 VminRHAA 0.000 0.327 VminRHAA 0.005 0.296 BH3 0.816 BH3 0.744 VmaxLAFEm 0.000 0.288 VmaxLAFE 0.000 0.224 VmaxRShIE 0.000 0.237 VmaxRShAA 0.000 0.511 VmaxRShAA 0.000 0.433 VmaxLShFE 0.000 -0.258 VminRShIE 0.000 -0.223 VminRShIE 0.000 -0.349 VminLHAA 0.000 -0.402 VminLHAA 0.000 -0.418 In used regression methodology (Y) is related to predictors (Xi) according to the mean function: Y = α + bi . Xi +, where alfa is the intercept, bi are the coefficients of regression for the i-predictor and β is the standard error of the estimation. The squared multiple correlation of the model (R2) explains % of the variability of the data. p - the level of significance, β= the beta correlation coefficient.