5 6 7 8 9 10 11 12 (For Problems 4, 5, and 6) John, the owner of Caf Ruben, is interested in weekly number of customers. He obtained the following table, showing the number of customers for the last 10 weeks. Week # of customers 1 161 207 217 201 5 157 6 207 163 203 190 144 2 3 4 7 8 9 10 He uses 5-week simple moving average to forecast the number of customers in week 11, then he customers forecasted in week 11. (if necessary, calculate to 1 decimal place, such as xxx.x) gets -stion 5 John is disappointed at the end of wook 11 because what he forecasted using a 5-week simple MacBook Air . FO 50 DUO F4 F3 A $ 4 % 5 & 7 1 0 8 9 6 7 8 163 203 190 144 9 10 He uses 5-week simple moving average to forecast the number of customers in week 11, then customers forecasted in week 11. (if necessary, calculate to 1 decimal place, such as xxx.x) gets estion 5 ut yet swered pints out of .00 John is disappointed at the end of week 11 because what he forecasted using a 5-week simple moving average was very different from the actual number of customers 149 in week 11. His associate Susan suggested John to use exponential smoothing to forecast the number of customers in week 12. Assume the forecast for week 11 is 144, he gets customers as a forecast for week 12 using an exponential smoothing method with a = 0.4. (Calculate to 2 decimal places if necessary.) Flag question Previous page Next page MEBOOK Quo 20 11 F3 F2 FYO $ 4 % 5 & 7 6 8 9 0 5 6 7 8 9 10 11 12 John has been using an exponential smoothing forecast method with a = 0.4 and has forecasted for weeks 12 to 15. Now week 15 is over. The table below shows the number of customers actually visited per week for weeks 11 to 15. His forecast of the number of customers for week 16 is customers if he continues using an exponential smoothing forecast method with a 0,4 (Calculate to 2 decimal places if necessary) Week # of customers 11 149 12 201 13 227 14 155 15 177 16 MacBook OUD FO F7 F5 A $ % 5 & 7 9 8 0 6 n P