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.