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 a. Is Model 2 better than Model 1? Explain why. What is the most significant variable in the regression? b. 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