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Extracted text: Data of Best Companies to Work For FT Total Worldwide Voluntary Total FT Revenues Turnover ($millions) 1,071.000 11,220.000 382.000 Jobs Added (%) 249 15.064 - 224 5.032 281 11.221 460 33,000.000 4.763 - 19 686.000 9.721 2,402 24,400.000 11.673 161 373.693 5.400 888.000 10.837 - 207 2,321.000 3,024.095 9,502.000 2,470.720 9,374.000 34,163.000 5.844 75 2.806 60 4.908 523 6.471 - 122 6.802 309 0.000 252 860.815 10.310 95 706.757 10.535 11,700.000 3,700.000 46,100.000 31,600.000 7,082 9.603 238 14.821 207 5.728 - 129 8.535 -
Extracted text: 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 X, represent the FT Voluntary Turnover (%). OX1 + (DX21 = + i (Round the constant and X2;-coefficient to the nearest integer as needed. Round the X4;-coefficient to four decimal places as needed.)