Use the data from this problem to perform the following $($note: your Weeks are in all one column $1$ to $12,$ then second column is Passenger Miles for all $12$ time periods$)$ ie make Column $1$ the week, Column $2$ is the miles... you will create Column $3$ forecast for #A$,$ Column $4$ Error for #A $($so can figure MAD$),$ Column $5$ forecast for #B$,$ Column $6$ Error for #B$,$ Column $7$ forecast for #C$,$ Column $8$ Error for #C etc. Saves you from having to repeat the provided data multiple times $($unless you wish to separate them$)$ A$)$ Using a weighted moving average with $5$ time periods, forecast the miles for week $13.$ Use a weight of $3$ for the most recent, a weight of $3$ for the next most recent, a weight of $2$ for the next most, a weight of $1$ for the next most, and a weight of $1$ for the next ie $3,3,2,1,1$ respectively. B$)$ Assuming an initial forecast for week $1$ of $17,000$ miles, use exponential smoothing to forecast miles for week $13.$ Use alpha $=\mathrm{.}2.$ C$)$ Assuming an initial forecast for week $1$ of $17,000$ miles, use exponential smoothing to forecast miles for week $13.$ Use alpha $=\mathrm{.}6.$ D$)$ Calculate the MAD for each forecasting technique and determine which is the most accurate? Why?