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 Smillions), and the full-time voluntary turnover (%). Use the accompanying data to complete parts (a) through (d) below. E Click the icon to view the data table. a. State the multiple regression equation. Let X, represent the Total Worldwide Revenues (Smillions) and let X, represent the FT Voluntary Turnover (%). O Data of Best Companies to Work For - X (Round the constant and X2-coefficient to the nearest integer as needed. Round the X,-coefficient to four decimal places as needed.) FT Revenues Jobs Added (Smillions) 7,998.700 Total Worldwide Voluntary Turnover (%) 8.329 Total FT - 973 1,160 3,651.328 23,420.000 888.000 276.616 332.268 6,841.496 608.200 11,500.000 1,931.000 6.168 14.355 10.837 6.375 -111 21 263 138 71 - 57 - 128 - 116 486 727.908 12,151.797 6,368.000 5,040.000 24,400.000 1,809.017 27,706.772 551.000 9,502.000 14,778.000 1.950 6.007 5.790 5.390 7.853 15.486 8.870 11.451 6.496 11.673 5.529 3.346 12.383 4.908 254 2,402 102 1,357 36 60 842 4.611
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 Smillions), and the full-time voluntary turnover (%). Use the accompanying data to complete parts (a) through (d) below. E Click the icon to view the data table. a. State the multiple regression equation. Let X, represent the Total Worldwide Revenues (Smillions) and let X, represent the FT Voluntary Turnover (%). O Data of Best Companies to Work For - X (Round the constant and X2-coefficient to the nearest integer as needed. Round the X,-coefficient to four decimal places as needed.) FT Revenues Jobs Added (Smillions) 7,998.700 Total Worldwide Voluntary Turnover (%) 8.329 Total FT - 973 1,160 3,651.328 23,420.000 888.000 276.616 332.268 6,841.496 608.200 11,500.000 1,931.000 6.168 14.355 10.837 6.375 -111 21 263 138 71 - 57 - 128 - 116 486 727.908 12,151.797 6,368.000 5,040.000 24,400.000 1,809.017 27,706.772 551.000 9,502.000 14,778.000 1.950 6.007 5.790 5.390 7.853 15.486 8.870 11.451 6.496 11.673 5.529 3.346 12.383 4.908 254 2,402 102 1,357 36 60 842 4.611
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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![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 (%).
Data of Best Companies to Work For
%3D
(Round the constant and X2-coefficient to the nearest integer as needed. Round the X,;-coefficient to four decimal places as needed.)
FT
Total Worldwide Voluntary
Turnover
Total FT
Revenues
($millions)
7,998.700
3,651.328
23,420.000
888.000
(%)
8.329
6.168
14.355
10.837
6.375
Jobs Added
- 973
1,160
- 111
21
276.616
263
332.268
1.950
138
6.007
6,841.496
608.200
71
5.790
- 57
- 128
- 116
5.390
11,500.000
1,931.000
727.908
7.853
15.486
12,151.797
6,368.000
5,040.000
24,400.000
1,809.017
27,706.772
551.000
486
8.870
11.451
254
6.496
11.673
2,402
102
5.529
1,357
36
3.346
12.383
60
4.908
9,502.000
14,778.000
842
4.611
Print
Done
Enter your answer in the edit fields and then click Check Answer.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F752ed300-1989-4759-8700-fdba4e5b0ee0%2F233bd735-510e-47d8-825f-51fb06894433%2Fxpzhqor_processed.png&w=3840&q=75)
Transcribed Image 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 (%).
Data of Best Companies to Work For
%3D
(Round the constant and X2-coefficient to the nearest integer as needed. Round the X,;-coefficient to four decimal places as needed.)
FT
Total Worldwide Voluntary
Turnover
Total FT
Revenues
($millions)
7,998.700
3,651.328
23,420.000
888.000
(%)
8.329
6.168
14.355
10.837
6.375
Jobs Added
- 973
1,160
- 111
21
276.616
263
332.268
1.950
138
6.007
6,841.496
608.200
71
5.790
- 57
- 128
- 116
5.390
11,500.000
1,931.000
727.908
7.853
15.486
12,151.797
6,368.000
5,040.000
24,400.000
1,809.017
27,706.772
551.000
486
8.870
11.451
254
6.496
11.673
2,402
102
5.529
1,357
36
3.346
12.383
60
4.908
9,502.000
14,778.000
842
4.611
Print
Done
Enter your answer in the edit fields and then click Check Answer.
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