Air Carrier Traffic Statistics Monthly is a handbook of airline data published by the U.S. Department of

Question:

Air Carrier Traffic Statistics Monthly is a handbook of airline data published by the U.S. Department of Transportation. In this book you will find revenue passenger-miles (RPM) traveled on major airlines on international flights. Airlines regularly try to predict accurately the RPM for future periods; this gives the airline a picture of what equipment needs might be and is helpful in keeping costs at a minimum.
The revenue passenger-miles for international flights on major international airlines is shown in the accompanying table for the period Jan-1979 to Feb-1984. Also shown is personal income during the same period, in billions of dollars.

a. Build a multiple-regression model for the data to predict RPM for the next month. Check the data for any trend, and be careful to account for any seasonality. You should easily be able to obtain a forecast model with an P-squared of about 0.70 that exhibits little serial correlation.
b. Use the same data to compute a time-series decomposition model, and again forecast for one month in the future.
c. Judging from the root-mean-squared error, which of the models in parts (a) and (b) proved to be the best forecasting model? Now combine the two models, using a weighting scheme like that shown in Table 8.1. Choose various weights until you believe you have come close to the optimum weighting scheme. Does this combined model perform better (according to RMSE) than either of the two original models? Why do you believe the combined model behaves in this way?
d. Try one other forecasting method of your choice on these data, and combine the results with the multiple-regression model. Do you obtain a better forecast (according to RMSE) than with either of your two original models?