We look at a final few linear models before we move on to some level of time series forecasting because the nature of the data largely shows the same results and I find it largely due to the nature and manner of the data collection is what causes the the outcomes in the high R squared values and the adjust R values.
So as a final linear model in the economic indicators data we can look at the same linear model that we have been analysing before but we can add to its complexity by adding interaction terms and other things such as the logan passengers and international flights data to futher enhance the complexity of the model and perhaps better enchance the prediction capabilities of the model.
We add the passenger term to our 3 term linear model and therefore making it 4 terms now.
Thus from the value we can tell while the value of P are low enough for passsenger data they are the only significant values as the passenger data even though marginally does increase the R square adjusted value to be into the 0.8s and it was not there before.
The international flights and passenger data have high enough P values that we can ignore them while considering our model so we can just stick with out initial 2 parameter model.