The problem facing a manager is to assess the impact of factors on full-time (FT
ID: 3053881 • 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 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 $millions), and the full-time voluntary turnover (%). Use the accompanying data to complete parts
State the multiple regression equation.
Let X1 represent the Total Worldwide Revenues ($millions) and let X2 represent the FT Voluntary Turnover (%).
95 706.757 10.535
138 6,841.496 6.007
228 25,364.000 5.617
2,013 5,027.418 7.753
569 1,309.239 6.996
86 696.200 5.233
716 2,331.041 8.335
-2 731.000 10.655
-53 2,238.927 6.492
-57 11,500.000 5.390
66 16,500.000 1.743
-19 686.000 9.721
1,465 53,300.000 0.388
-106 10,900.000 3.856
1,654 10,960.000 15.644
486 12,151.797 8.870
55 280.000 7.688
460 33,000.000 4.763
0 6,368.000 11.451
-1,261 11,762.000 0.000
Explanation / Answer
If X1 represents the Total Worldwide Revenues ($millions) and X2 represents FT Voluntary Turnover (%) and Y represents the number of full-time jobs added in a year, then the multiple regression equation is given by
Y= - 813.05459 + 0.02995*X1 + 116.77069*X2
The regression coefficients can be find out using R programming.
The code is given below;
a<-c(95,138,228,2013,569,86,716,-2,-53,-57,66,-19,1465,-106,1654,
486,55,46,0,-1261)
b<-c(706.757,6841.496,25364,5027.418,1309.239,696.200,2331.041,731,
2238.927,11500,16500,686,53300,10900,10960,12151.797,280,33000,
6368,11762)
d<-c(10.535,6.007,5.617,7.753,6.996,5.233,8.335,10.655,6.492,5.390,
1.743,9.721,.388,3.856,15.644,8.870,7.668,4.763,11.451,0)
e<-rep(1,20)
m<-data.frame(a,b,d)
m
x<-cbind(e,b,d)
y<-matrix(a)
s<-solve(t(x)%*%x)%*%t(x)%*%y
s
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