The owner believes that the number of customers served 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 Albany Airport, 2) the daily high temperature outside. The following table shows the statistics from the month of September. Date Daily Number of Visitors / Arrived at Albany Airport Daily High Temperature (in Fahrenheit) / Number of customers to the Stall 9/1 37,223 80 351 9/2 37,354 81 358 9/3 38,662 82 380 9/4 36,995 75 335 9/5 37,845 76 352 9/6 37,452 74 350 9/7 35,542 75 320 9/8 37,983 78 360 9/9 36,058 72 341 9/10 35,084 75 315 9/11 37,548 75 352 9/12 37,787 76 353 9/13 36,584 82 330 9/14 38,698 83 351 9/15 37,985 84 366 9/16 38,684 85 361 9/17 36,989 82 358 9/18 37,947 86 359 9/19 35,868 78 328 9/20 35,879 77 329 9/21 38,587 76 351 9/22 37,459 75 350 9/23 35,978 79 329 9/24 37,687 79 355 9/25 36,852 82 345 9/26 38,742 84 370 9/27 38,742 80 379 9/28 38,662 82 375 9/29 38,312 83 352 9/30 38,498 81 359 a) If casual model based regression is used to forecast, identify the variable NAMES for Y (predicted) and X (predictors). Y: X1: X2: b) Copy and paste the data to Excel, conduct a regression analysis, paste the Excel regression output below. c) Write down your forecasting equation below. d) If you know for Oct 10th, the expected total number of visitors to Albany is 38,500, the forecasted daily high temperature is 82, what would be your forecasted number of customers to the food stall