I want to solve the exercise in the attached picture

434 Chapter Eight (RPM) traveled on major airlines on international flights Airlines regularly try to pre- dict accurately the RPM for future periods, this gives the airline a picture of what equip ment needs might be and is helpful in keeping costs at a minimum The revenue passenger-miles for international flights ca major international airlines is shown in the accompanying table for the period Jan-1979 - Feb-1984. Also shown is personal income during the same period in billions of dollars. Date Personal Income 2,4334 2.4626 2.4735 2,4875 2.492.1 2.499.1 2,5138 2.5186 2.5355 2.5562 2.5663 Jan-1979 Feb-1979 Mar-1979 Apr 1979 May 1979 Jun-1979 Jul-1979 Aug.1979 Sep-1979 Oct-1979 Nov-1979 Dec-1979 Jan-1980 Feb-1980 Mar 1980 Apr 1980 May 1980 lan-1980 Jul-1980 Aug. 1980 Sep-1980 Oct-1980 Nov.1980 Dec-1980 Jan-1981 Feb-1981 Mar-1981 Apr-1981 May 1981 Jun-1981 Jul-1981 RPM 4,114,904 3,283,488 4,038,611 4,312,697 4,638,300 6,661,979 6,221,612 6,489,078 5,258,750 4,720,027 4,037,529 4,240,862 4,222,446 3,540,027 4,148,262 4,106,723 4,602,599 5,169,789 5,911,035 6,236,392 4,700,133 4,274,816 3,611,307 3,794,631 3.513,072 2,856,083 3,281,964 3,694,417 4,240,501 4,524 445 5,156,871 Personal Income 1,8343 1.8514 1,872.1 1.880.7 1.891.6 1.9051 1.9332 1.946.5 1.960.1 1,9792 2,000.0 2,022.5 2,0772 2.086.4 2.1010 2.102.1 2.114.1 2.127.1 2.161.2 2.179.4 2,205.7 22353 2.260.4 2,281.5 2.3007 2.318.2 2.340 4 2,353,8 2.3674 2,3843 24192 Date Aug-1981 Sep-1981 Oc1-1981 Nov.1981 Dec-1981 Jan-1982 feb-1982 Man 1982 Apr-1982 May-1982 Jun-1982 Jul-1982 Aug-1982 Sep-1982 Oct-1982 Nov-1982 Dec. 1982 Jan-1983 Feb-1983 Mar-1983 Apr 1983 May-1983 Jun-1983 l-1983 Aug-1983 Sep-1983 Oct-1983 Nov-1983 Dec-1983 Jan-1984 Feb-1984 RPM 5.465,791 4.320,529 4,036,149 3.272.074 3.514227 3.558.273 2,834,658 3.318.250 3.660,038 4,014 541 4,487.598 5.088.561 5.292.201 4.320.181 4,069.619 3.125,650 3,381,049 3.513,758 2,876,672 3.536.871 3.744 695 4.404999 5.201.363 5.915.482 6.022431 5,000,685 4,659,152 3.592.160 3,818.737 3.828.367 3.221633 2,5883 2.5920 2.5972 2,5115 2.621.3 2.6368 2.552.6 2.5505 2.570.1 2,599.0 2.7193 2.732.5 2.7476 2.7564 2,7815 23128 2.333.1 28572 2.897.4 29235 2. 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 R-squared of about 0.70 that exhibits little serial correlation 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 (c) and (b) proved to be the best forecasting modet? Now combine the two models, sing