Many businesses are affected by the number of airline passengers, including the airlines themselves, plus hotels, restaurants, and the entertainment industry. It is therefore important to keep track of trends and make forecasts about the number of airline passengers. The data file contains the number of Canadian airline passengers from July 2006 to November 2009.

a) Plot the data and identify the seasonal pattern.

b) Fit an autoregressive model to the data, including one term with a single lag and another term with a lag corresponding to the seasonality. What is the first month you can use to fit the model? Give the result of the regression analysis and the model equation. Is the seasonality significant?

c) Forecast the number of Canadian airline passengers in December 2009.