We concluded from our previous analyses of the various plots that we had obtained from the analysis of the hotel occupation rates and the hotel average daily rates and how we could relate the two to predict overall trends and how they can be seen.
So we saw that the linear model did not accurately predict a lot but it did do reasonably well for the flexibility that the model offered in terms of it’s simplicity. Compared to all our other data that we have we try to complicate the model further by making it so that we can add more variables.
To predict the hotel rate we try to do it using occupancy and international flights and total passengers and logan airport.
We hope these additions as most travelers should be staying in hotels should relate well to hotel rates and occupancy numbers.
From the R squared values we are able to tell that the the fit value got better because it is nearly 0.8 now and it shows a higher relation.
The p values for the introudced new term also shows that it’s significant due to a low enough P value.