The problem facing a manager is to assess the impact of factors on full-time (FT
ID: 2946040 • Letter: T
Question
The problem facing a manager is to assess the impact of factors on full-time (FT) job growth. Specifically, the manager is interested 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.
13.780
State the multiple regression equation.
Yi= ans +(ans)X1i +(ans)X2i
(Round the constant and X 2 iX2i -coefficient
to the nearest integer as needed. Round the
X 1 iX1i -coefficient
to four decimal places as needed.)
Total FT Jobs Added Total Worldwide Revenues FT Voluntary Turnover -19 686 9.721 161 373.693 5.4 4040 21396 13.136 360 1543 8.582 -237 5955.676 4.285 36 551 12.383 178 3900 5.99 -111 23420 14.355 87 290.401 4.559 2013 5027.418 7.753 1465 53300 0.388 -973 7998.7 8.329 71 608.2 5.79 -64 4487 8.322 55 280 7.688 716 2331.041 8.335 2482 19121 2.792 314 3319 18.283 -129 31600 8.535 115 609.24813.780
Explanation / Answer
We denote y:Total FT jobs added ; x1:Total world wide revenues ; x2 : FT voluntary turnover
On regresing y on x1 and x2 from the given data we get the following fitted regresion equation.
Y = 137.2552 + 0.0315 *x1 + 11.4661 *x2 ; Y is the predicted value of y.
Thus, on rounding of the regression coefficients we get the following,
Y = 137 + 0.0315 *x1 + 11 * x2;
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