Shown below is the output of two regression analyses on the same problem. The first output was done on a "full" model. In the second output, the variable with the highest p-value was removed and the regression has been rerun. Examine the two outputs.

Model 1: R-squared = 0.321; adjusted R-squared = 0.208

Independent Variables	Coefficients	p-value
Intercept	336.790	0.012
X1	1.650	0.363
X2	-5.630	0.680
X3	0.260	0.878
X4	185.500	0.010

Model 2: R-squared = 0.311; adjusted R-squared = 0.239

Independent Variables	Coefficients	p-value
Intercept	342.919	0.006
X1	1.834	0.174
X2	-5.749	0.667
X4	181.220	0.005

1. Is Model 2 better than Model 1? Explain why. What is the most significant variable in the regression?
2. Suppose we continue with this process of backward elimination where we remove the variable with the highest p-value. Which variable is likely to become newly significant as a result of this step? Does this imply that we have a better model?