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Spreadsheet Application: A Da Mi 1-35. TE d! t 1-34. The local travel center, Campus Travel, has been losing sales. The presence of online ticketing websites, such as Travelocity.com and Expedia.com, has lured many students away. However, given the complexity of mak- ing international travel arrangements, Campus Travel could have a thriving and profitable business if it con- centrated its efforts in this area. You have been asked by the director of sales and marketing to help with ana- lyzing prior sales data in order to design better market- ing strategies. Looking at these data, you realize that it is nearly impossible to perform a detailed analysis of ticket sales given that the data are not summarized or organized in a useful way to inform business decision making. The spreadsheet TicketSales.csv contains the ticket sales data for a 3-month period. Your director has asked you for the following information regarding ticket sales. Modify the TicketSales.csv spreadsheet to provide the following information for your director: The total number of tickets sold. a. Select the data from the tickets sold" column. b. Then select the "autosum" function. The largest amount of tickets sold by a certain salesperson to any one location. a. Select the appropriate cell. b. Use the "MAX" function to calculate each sales- person's highest ticket total in one transaction. The least amount of tickets sold by a certain sales- person to any one location. a. Select the appropriate cells. b. Use the "MIN" function to calculate the least tickets sold." fo Se18 The average number of tickets sold. a. Select the cells. b. Use the "AVERAGE" function to calculate the "average number of tickets sold" using the same data you had selected in the previous steps. Database Application: Tracking Frequent-Flier Miles at the Campus Travel Agency A 1-35. The director of sales and marketing at the travel agency would like to increase the efficiency of han- dling customers who have frequent-flier accounts. Often, frequent fliers have regular travel routes and a preferred seating area or meal category. In previ- ous years, the data have been manually entered into a E B D 1 Date Sold Salesperse Destination Tickets Sold 2 2/15/2011 Lisa France (P: 3 3 3/3/2011 Bob Hungary a 2 4 1/28/2011 Lisa Thailand o 4 5 1/15/2011 Bob Japan 2 6 3/6/2011 Vivien India 3 7 2/1/2011 John Middle Eas 1 8 4/5/2011 Andrew Egypt and 1 9 2/11/2011 Bob India 2 10 2/9/2011 John Egypt and 2 11 1/19/2011 John Thailand o 3 12 1/30/2011 Heather Hungary a 3 13 2/27/2011 Vivien Middle Eas 4 14 3/13/2011 John Japan 3 15 4/13/2011 Lisa Japan 2 16 4/1/2011 John France (P: 1 17 3/28/2011 Lisa Egypt and 2 18 3/16/2011 John India 4 19 4/22/2011 Vivien Thailand o 6 20 4/5/2011 John Hungary a 2 21 4/9/2011 Vivien France (P: 1 22 3/6/2011 Vivien Egypt and 2 23 2/2/2011 Lisa India 3 24 1/23/2011 Vivien Hungary a 1 25 3/3/2011 Andrew Middle Eas 3 26 1/2/2011 Andrew Japan 2 27 1/19/2011 Andrew France (P: 2 12 1/30/2011 Heather 13 2/27/2011 Vivien 14 3/13/2011 John 15 4/13/2011 Lisa 16 4/1/2011 John 17 3/28/2011 Lisa 18 3/16/2011 John 19 4/22/2011 Vivien 20 4/5/2011 John 21 4/9/2011 Vivien 22 3/6/2011 Vivien 23 2/2/2011 Lisa 24 1/23/2011 Vivien 25 3/3/2011 Andrew 26 1/2/2011 Andrew 27 1/19/2011 Andrew 28 4/2/2011 Bob 29 4/12/2011 Lisa 30 4/1/2011 Lisa 31 3/16/2011 Vivien 32 3/11/2011 Bob 33 2/11/2011 Andrew 34 2/19/2011 Bob 35 2/19/2011 Heather 36 4/15/2011 Heather 37 Hungary a 3 Middle Eas 4 Japan 3 Japan 2 France (P: 1 Egypt and 2 India 4 Thailand o 6 Hungary a 2 France (P: 1 Egypt and 2 India 3 Hungary a 1 Middle Eas 3 Japan 2 France (P: 2 France (P 1 Hungary a 3 Middle Eas 3 Japan 3 Egypt and 2 India 1 Middle Eas 2 Egypt and 1 Thailand o 1 Total Tickets Sold Most Tickets Sold Least Tickets Sold Average Number of Tickets Sold 38 39 40 41 42 43 Ch 1 TicketSales