-Shown below is output from two Excel regression analyses on the same problem. The first output was done on a "full" model. In the second output, the variable with the smallest absolute t value has been removed, and the regression has been rerun as a second step of a backward elimination process. Examine the two outputs. Explain what happened, what the results mean, and what might happen in a third step. FULL MODEL Regression Statistics Multiple F 0.567 R Square 0.321 Adjusted R Square 0.200 Standard Error 59.691 Observations ANOVA MS Significance F Regression 269856.08 72464.02 2.84 0.046 Residual 24 61 1955.23 25496.13 Total 78 901811.31 Coefficients Standard Error I Stat Povalue Intercept 336.79 124.0000 2.71 0.012 *1 1.65 1.7800 0.93 0.363 -5.63 13.4700 -0.42 0.680 0.26 1.6800 0.16 0.87 185.50 66.2200 2.80 0.010 SECOND MODEL Regression Statistics Multiple P 0.566 R Square 0.321 Adjusted R Square 0.239 Standard Error 156.534 Observations 29 ANOVA SS MS F Significance F Regression 3 289238.10 96412.70 3.93 0.020 Residua 25 612573.20 24502.90 Total 28 901811.30 Coefficients Standard Error t Stat P-value Intercept 342.92 115.34 2.97 0.006 X1 1.83 1.31 1.40 0.174 X2 -5.76 13.18 -0.44 0.667 YA 181.22 59.05 3.07 0.005 R2 for the full model = R2 after dropping out x3 =