Do each of the predictor variables improve the model? Explain how you know this is true. Model Summary Change Statistics Adjusted R Square Std. Error of the Estimate R Square Change Sig. F Change R R Square F Change df2 Model df1 .111" .012 .012 3.057 .012 22.185 1771 .000 2 111 .012 .011 3.058 .000 .002 1770 .969 3 462° 214 .212 2.729 .201 452.727 1769 .000 4 494 244 243 2.676 .031 71.763 1768 .000 a. Predictors: (Constant), NonWhite b. Predictors: (Constant), NonWhite, Female c. Predictors: (Constant), NonWhite, Female, Highest year school completed, father d. Predictors: (Constant), NonWhite, Female, Highest year school completed, father, Highest year school completed, mother Coficients" Standardized Coeficients Unstandardized Coeficients Collinearity Statistics Model Std. Error Beta Sig. Tolerance VIF (Constant) 14.262 .081 175.079 .000 Nonwhite 846 180 .111 4.710 .000 1.000 1.000 (Constant) 14.259 .112 126.835 .000 NonWhite 846 180 111 4.707 .000 .999 1.001 Female 006 .146 001 039 969 999 1.001 (Constant) 10.184 216 47.102 .000 NonWhite 283 .163 .037 1.741 .082 972 1.029 Female 090 130 .015 690 490 998 1.002 Highest year school completed, father 331 .016 455 21.277 .000 .972 1.029 (Constant) 9.301 236 39.367 .000 NonWhite 229 .160 .030 1.434 .152 .971 1.030 Female 120 023 1.126 260 995 1.005 144 Highest year school completed, father 209 .021 288 9.981 .000 .515 1.942 Highest year school completed, mother 194 .023 244 8.471 .000 517 1.934

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