. 2018 May 14;17(2):330–338.

Table 5.

Chosen results of the best subset multiple regression methodology.

VRmax					VRcont
Sample	R²	X_i	p	β	Sample	R²	X_i	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 (X_i) according to the mean function: Y = α + b_i ^. X_i +, where alfa is the intercept, b_i 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.