Create a linear trend forecast for the next ten games. Hint: You will have to create a linear

trend equation for each game of the season across all six years. In other words, use all six

years of info for Game $1$ to create a Game $1$ trend equation. Then repeat for Game $2$

through Game $5.$ Once you have the equation for each of the five games, a forecast can be

made for each game across the next two years to give you ten game forecasts.

For example:

Compute seasonal indices for each game. Use this to adjust the linear trend forecast

generated in the previous step for the next ten games. Hint: Use games as "seasons" in the

six years of data, so treat each game like a month in a year in your calculations.

Use the forecast and information in the case on ticket prices to project revenue for the next

two seasons.$\backslash$table$[[,2016,2017,2018],[$GAME$,$ATTENDEES,OPPONENT,ATTENDEES,OPPONENT,ATTENDEES,OPPONENT$],[1,34,200,$Rice,$36,100,$Miami,$35,900,$USC$],[2\_(-)^($a$),39,800,$Texas,$40,200,$Nebraska,$46,500,$Texas Tech$],[3,38,200,$Duke,$39,100,$Ohio State,$43,100,$Alaska$],[4\_(-)^($b$),26,900,$Arkansas,$25,300,$Nevada,$27,900,$Arizona$],[5,35,100,$TCU,$36,200,$Boise State,$39,200,$Baylor$]]$

$\backslash$table$[[,2019,2020,2021],[$GAME$,$ATTENDEES,OPPONENT,ATTENDEES,OPPONENT,ATTENDEES,OPPONENT$],[1,41,900,$Arkansas,$42,500,$Indiana,$46,900,$LSU$],[2\_(-)^($a$),46,100,$Missouri,$48,200,$North Texas,$50,100,$Texas$],[3,43,900,$Florida,$44,200,$Texas A&M$,45,900,$South Florida$],[4\_(-)^($b$),30,100,$Central Florida,$33,900,$Southem,$36,300,$Montana$],[5,40,500,$LSU,$47,800,$Oklahoma,$49,900,$Arizona State$]]$

SWU's president, Dr$.$ Joel Wisner, decided it was time for his vice president of development to forecast when the existing stadium would "max out." The expansion was, in his mind, a given. But Wisner needed to know how long he could wait. He also sought a revenue projection, assuming an average ticket price of $$50$ in $2022$ and a $5\%$ increase each year in future prices.