A B C D E F G H | J K L M N 0 P Q R 5 3 After examining your forecasted values in problem 1, the owner of the stall does not appear to be completely satisfied. She believes that the number of customers served (rather than number of 4 hamburgers sold) is a parameter she is more interested in forecasting. To help her out, you have researched all possible factors and found out two that are most relevant: 1) the daily total number of visitors arrived at Boston Logan Airport, 2) the daily high temperature outside. The following table shows the statistics from the month of September. 6 Daily Number of Daily High Number of Date Visitors Temperatur customers Arrived at Logan e (in Fahrenheit) to the Stall 7 Airport 8 1-Sep 37,223 80 351 9 2-Sep 37,354 81 358 10 3-Sep 38,662 82 380 11 4-Sep 36,995 75 335 12 5-Sep 37,845 76 352 13 6-Sep 37,452 74 350 14 7-Sep 35,542 75 320 15 8-Sep 37,983 78 360 16 9-Sep 36,058 72 341 17 10-Sep 35,084 75 315 18 11-Sep 37,548 75 352 19 12-Sep 37,787 76 353 20 13-Sep 36,584 82 330 21 14-Sep 38,698 83 351 22 15-Sep 37,985 84 366 23 16-Sep 38,684 85 361 24 17-Sep 36,989 82 358 25 18-Sep 37,947 86 359 26 19-Sep 35,868 78 328 27 20-Sep 35,879 77 329 28 21-Sep 38,587 76 351 29 22-Sep 37,459 75 350 30 23-Sep 35,978 79 329 31 24-Sep 37,687 79 355 32 25-Sep 36,852 82 345 33 26-Sep 38,742 84 370 34 27-Sep 38,742 80 379 35 28-Sep 38,662 82 375 36 29-Sep 38,312 83 352 37 30-Sep 38,498 81 359 38 39 a) If casual model based regression is used to forecast, identify the variable NAMES for Y (predicted) and X (predictors). List the variable names below: S T U > W X Y N AA