Shown below is output from two Excel regression analyses on the same problem. The first output was done on a €œfull€ 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 thirdstep.

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FULL MODEL Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.567 0.321 0.208 159.681 29 ANOVA df MS F SignificanceF Regression 4 289856.08 72464.02 2.84 Residual 0.046 24 611955.23 25498.13 28 901811.31 Coefficients Standard Errort Stat P-value Intercept X1 X2 X3 X4 336.79 1.65 5.63 0.26 185.50 124.0800 1.7800 13.4700 1.6800 66.2200 2.71 0.93 -0.42 0.16 2.80 0.012 0.363 0.680 0.878 0.010 SECOND MODEL Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.566 0.321 0.239 156.534 29 ANOVA F SignificanceF Regression 3 289238.1096412.70 3.93 Residual 0.020 25 612573.20 24502.90 28 901811.30 Coefficients Standard Error t Stat P-value Intercept X1 X2 X4 342.92 1.83 5.75 181.22 115.34 1.31 13.18 59.05 2.97 1.40 -0.44 3.07 0.006 0.174 0.667 0.005