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 like 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 Significance MS 72464.02 25498.13 df Regression Residual Total 289856.08 611955.23 901811.31 2.84 0.046 28 Coefficients Standard Error t Stat P.value 2.71 0.93 13.4700 -0.42 0.16 2.80 0.012 0.363 0.680 0.878 0.010 Intercept 336.79 1.65 -5.63 0.26 185.50 124.0800 1.7800 1.6800 66.2200 SECOND MODEL Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.566 0.321 0.239 156.534 29 ANOVA Significance MS 96412.70 24502.90 df Regression Residual Total 289238.1 612573.20 901811.3 3.93 0.020 25 28 Coefficients Standard Error t Stat P-value 0.006 0.174 0.667 0.005 11.34 1.31 Intercept 342.92 1.83 -5.75 181.22 2.97 1.40 13.18 -0.44 3.07 59.05