The problem facing a manager is to assess the impact of factors on full-time (FT) job growth. Specifically, the manager is interest in the impact of total worldwide revenues and full-time voluntary turnover on the number of full-time jobs added in a year. Data were collected from a sample of 20 "best companies to work for." The data includes the total number of full-time jobs added in the past year, total worldwide revenue (in $millions), and the full-time voluntary turnover (%). Use the accompanying data to complete parts (a) through (d) below. Click the icon to view the data table. a. State the multiple regression equation. Let X, represent the Total Worldwide Revenues ($millions) and let X2 represent the FT Voluntary Turnover (%). Y, = [+ () X1: + (X21 (Round the constant and X2;-coefficient to the nearest integer as needed. Round the X1;-coefficient to four decimal places as needed.) X Data of Best Companies to Work For Total FT Total Worldwide FT Voluntary Jobs Added Revenues ($millions)Turnover (%) - 64 4,487.000 8.322 - 122 9,374.000 6.802 1,264 12,316.379 20.038 1,264 3,050.195 16.188 84 961.478 6.534 161 373.693 5.400 59 1,498.124 2.784 6,368.000 11.451 110 11,682.636 4.677 1,886 20,729.000 7.451 95 706.757 10.535 55 280.000 7.688 249 1,071.000 15.064 - 19 686.000 9.721 71 608.200 5.790 223 746.000 12.064 87 290.401 4.559 66 16,500.000 1.743 2,013 5,027.418 7.753 - 16 380.941 11.827