Regression Statistics Multiple R 0.59523856 R Square 0.35430895 Adjusted RS 0.324622 Standard Err 15.8250705 Observations 92 ANOVA dif SS MS F Significance F Regression 4 11955.5046 2988.87615 11.9348403 8.9382E-08 Residual 87 21787.6584 250.432856 Total 91 33743.163 Coefficients itandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 250.477518 42.8739301 5.84218701 8.7453E-08 165.260939 335.694096 165.260939 335.694096 WEIGHT 0.45512778 0.09300196 4.89374402 4.507E-06 0.27027632 0.63997924 0.27027632 0.63997924 HEIGHT -1.8605482 0.8719401 -2.1338028 0.03567342 -3.5936236 -0.1274729 -3.5936236 -0.1274729 LEG 0.01074461 1.92610706 0.00557841 0.99556187 -3.8176017 3.83909096 -3.8176017 3.83909096 THIGH -7.6676632 1.28525568 5.965866 5.1206E-08 -10.222248 -5.1130783 -10.222248 -5.1130783 By looking at the excel output produced for the model B, John concluded that the coefficient of "WEIGHT" variable is positive (0.455), suggesting that weightier people are older ( or younger people are fit). Why does he see such results? O Because of the weak correlation that exists between LEG and WAIST. Because of the high correlation of WAIST and WEIGHT. O An important variable is excluded in the model B. O Perhaps he made a mistake