a. including the D variable in model 2 results in a much larger adjusted R2, suggesting that the inclusion of the dummy variable is necessary to boost predictive power.b. the coefficient estimates for t and t2 change dramatically, even though the models are very comparable (unadjusted with a seasonal dummy is pretty close to seasonally adjusted).c. dropping the D variable in model 3 pulls the R2 down, which is unexpected since D in model 2 is statistically insignificant.d. the D variable in model 2 does a decent job of capturing the seasonal effect, since the results between the two models are not hugely different and D has the expected sign and is statistically significant.
2\. The regression results for model 4 are notable because
a. making the seasonal adjustment in the dependent variable, in addition to adding the D dummy, yields the best results in terms of significant coefficients, explanatory power, and expected signs.b. adding the redundant D variable to the seasonally adjusted data causes the coefficient estimates for t and t2 to be dramatically different than they were in models 2 and 3.c. the adjusted R2 is higher than in the comparable model 3 (without the D).d. adding a redundant seasonal dummy to already seasonally-adjusted data results in the D variable being insignificant, as expected, and the model’s explanatory power is essentially the same as models 2 and 3.