12. A regional supplier of jet fuel is interested in forecasting its sales. These sales data are shown for the period from 2002Q1 to 2017Q4 (data in billions of gallons): (cop12 2) ) Jet Fuel Sales (Billions of Gallons) Year Q1 Q2 03 Q4 2002 23.86 23.97 29.23 24.32 2003 23.89 26.84 29.36 26.30 2004 27.09 29.42 32.43 29.17 2005 28.86 32.10 34.82 30.48 2006 30.87 33.75 35.11 30.00 2007 29.95 32.63 36.78 32.34 2008 33.63 36.97 39.71 34.96 2009 35.78 38.59 42.96 39.27 2010 40.77 45.31 51.45 45.13 2011 48.13 50.35 56.73 48.83 2012 49.02 50.73 53.74 46.38 2013 46.32 51.65 52.73 47.45 2014 49.01 53.99 55.63 50.04 2015 54.77 56.89 57.82 53.30 2016 54.69 60.88 63.59 59.46 2017 61.59 68.75 71.33 64.88a. Prepare a time series graph of these data. What, if any, seasonal pattern do you see in the plot? Explain. b. Use ForecastXTM to make a time series decomposition forecast for 2018. Write a brief report explaining your forecast. Include a graph of the fitted values. the forecast values. and the actual sales. c. Develop two other forecasts ofjet fuel sales using the following methods: lA Winters' exponential smoothing model; and 2. A regression model usingjust time and quarterly dummy variables. Compare the MAPEs for the three models you have developed. and comment on what you like or dislike about each of the three models for this application