Absenteeism can be a serious employment problem. It is estimated that absenteeism reduces potential output by more than 10%. Two economists launched a research project to learn more about the problem. They randomly selected 100 organizations to participate in a 1-year study. For each organization, they recorded the average number of days absent per employee and several variables thought to affect absenteeism. The file ‘absenteeism FinEx’ contains the data on these selected 100 organizations. Question: How do
Absenteeism can be a serious employment problem. It is estimated that absenteeism reduces potential output by more than 10%. Two economists launched a research project to learn more about the problem. They randomly selected 100 organizations to participate in a 1-year study. For each organization, they recorded the average number of days absent per employee and several variables thought to affect absenteeism. The file ‘absenteeism FinEx’ contains the data on these selected 100 organizations. Question: How do
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Absenteeism: Absenteeism can be a serious employment problem. It is estimated that absenteeism reduces potential output by more than 10%. Two economists launched a research project to learn more about the problem. They randomly selected 100 organizations to participate in a 1-year study. For each organization, they recorded the average number of days absent per employee and several variables thought to affect absenteeism. The file ‘absenteeism FinEx’ contains the data on these selected 100 organizations.
Question: How does the MLR analysis compare with the t-test for Av Shift in predicting absenteeism? Comment on the similarities and/or differences. (both MLR and t-test are provided below)
Data:
| Wage | Pct PT | Pct U | Av Shift | U/M Rel | Absent |
| 22477 | 8.5 | 57.1 | 1 | 1 | 5.4 |
| 29939 | 1.9 | 41.5 | 0 | 1 | 4.1 |
| 22957 | 12.2 | 52.6 | 1 | 0 | 11.5 |
| 18888 | 30.8 | 65.1 | 0 | 1 | 2.1 |
| 15078 | 6.8 | 68.8 | 0 | 1 | 5.9 |
| 15481 | 5.1 | 46.4 | 0 | 0 | 12.9 |
| 21481 | 25.3 | 38.9 | 0 | 1 | 3.5 |
| 29687 | 9.2 | 17.2 | 0 | 0 | 2.6 |
| 13603 | 8.4 | 12.9 | 0 | 0 | 8.6 |
| 18303 | 4.9 | 18.1 | 0 | 1 | 2.7 |
| 20832 | 23.8 | 64.4 | 1 | 1 | 6.6 |
| 22325 | 24.1 | 63.7 | 1 | 1 | 2.1 |
| 19964 | 8.6 | 12.2 | 0 | 1 | 3.8 |
| 32496 | 5.9 | 11.8 | 1 | 0 | 4.3 |
| 15795 | 2.9 | 25.8 | 0 | 1 | 4.3 |
| 21138 | 24.3 | 53.2 | 0 | 0 | 2.2 |
| 18859 | 20.6 | 22.8 | 1 | 1 | 8.6 |
| 12023 | 9 | 49.8 | 1 | 1 | 10.8 |
| 33272 | 24 | 39.1 | 1 | 0 | 2.9 |
| 22325 | 11.9 | 32.6 | 1 | 0 | 5.3 |
| 26147 | 0 | 67.7 | 1 | 0 | 8.2 |
| 33229 | 11.7 | 10.8 | 0 | 0 | 2.8 |
| 37970 | 14.6 | 25.5 | 1 | 1 | 2.4 |
| 15281 | 27.2 | 31.8 | 0 | 0 | 2.8 |
| 19423 | 17.2 | 35 | 1 | 1 | 5 |
| 26587 | 13.9 | 41.9 | 1 | 1 | 9.5 |
| 22963 | 2.6 | 52.9 | 0 | 1 | 4.3 |
| 26404 | 6.4 | 64.4 | 0 | 1 | 8.9 |
| 16315 | 4.9 | 69.7 | 0 | 1 | 7.2 |
| 26759 | 23.2 | 61.8 | 1 | 1 | 5.6 |
| 30824 | 13.2 | 52.1 | 0 | 1 | 2.4 |
| 31979 | 27.7 | 57.4 | 1 | 1 | 2.7 |
| 23135 | 7 | 15.2 | 0 | 0 | 13.4 |
| 18014 | 0 | 38.7 | 1 | 0 | 14.8 |
| 18541 | 13.8 | 69.4 | 1 | 1 | 10.7 |
| 16747 | 9.9 | 67.2 | 1 | 0 | 10.3 |
| 13473 | 6.3 | 47.8 | 0 | 1 | 4.6 |
| 42986 | 13.4 | 24.5 | 1 | 0 | 3.9 |
| 23964 | 8.8 | 79.4 | 1 | 0 | 13.3 |
| 30794 | 0.4 | 12.1 | 1 | 0 | 2.2 |
| 21104 | 14.7 | 71 | 0 | 1 | 5.7 |
| 19137 | 7.7 | 28 | 1 | 0 | 11.8 |
| 26058 | 7.3 | 45.6 | 0 | 1 | 2.5 |
| 22085 | 6.8 | 25.4 | 0 | 1 | 2.1 |
| 29044 | 8.6 | 40.6 | 0 | 0 | 4.1 |
| 24205 | 19.6 | 25.1 | 1 | 1 | 4.9 |
| 17698 | 10.8 | 42.3 | 1 | 1 | 7.7 |
| 26399 | 4.5 | 63.3 | 1 | 1 | 6.3 |
| 40590 | 15.9 | 69.4 | 1 | 1 | 2.9 |
| 24805 | 5.7 | 17.7 | 1 | 1 | 2.6 |
| 18899 | 13.1 | 54.8 | 1 | 1 | 6.1 |
| 26802 | 15.5 | 46.5 | 0 | 1 | 6 |
| 30034 | 11.8 | 53.2 | 1 | 0 | 6.7 |
| 15713 | 16.6 | 41.2 | 1 | 0 | 11.9 |
| 18280 | 6.4 | 65 | 1 | 1 | 9.3 |
| 41009 | 6.7 | 54.9 | 0 | 1 | 3.6 |
| 24021 | 14 | 20.6 | 1 | 1 | 2.6 |
| 21836 | 27.6 | 29 | 0 | 1 | 2.1 |
| 21157 | 5.5 | 50.2 | 1 | 1 | 9 |
| 19529 | 14.5 | 56.6 | 1 | 0 | 11 |
| 31240 | 26.3 | 36.4 | 1 | 1 | 2.9 |
| 20963 | 0 | 0 | 1 | 1 | 2.2 |
| 33826 | 8.2 | 87.9 | 0 | 1 | 3.3 |
| 23349 | 0 | 38.5 | 1 | 1 | 5.9 |
| 22695 | 25.4 | 47 | 1 | 1 | 4 |
| 30475 | 0 | 69.3 | 1 | 0 | 10.8 |
| 16631 | 5.9 | 48.2 | 1 | 1 | 7.1 |
| 28996 | 18.6 | 29.3 | 1 | 1 | 2.9 |
| 15807 | 16.9 | 42.9 | 1 | 1 | 6.2 |
| 15585 | 0 | 59.4 | 1 | 0 | 10.3 |
| 18466 | 9 | 69.4 | 1 | 0 | 13.5 |
| 35140 | 21.1 | 37.1 | 1 | 1 | 6.7 |
| 33459 | 14.1 | 19.5 | 1 | 1 | 2.6 |
| 24357 | 0 | 21.5 | 1 | 1 | 5.2 |
| 19370 | 3.7 | 35 | 1 | 1 | 7.2 |
| 21820 | 6.3 | 0 | 1 | 1 | 3.5 |
| 23351 | 12.3 | 27.1 | 1 | 1 | 5.4 |
| 22938 | 6.8 | 68.5 | 1 | 1 | 5.8 |
| 16477 | 10 | 61.5 | 1 | 1 | 11.7 |
| 20790 | 28.5 | 59.9 | 1 | 0 | 5.6 |
| 20352 | 19.4 | 34.6 | 1 | 0 | 4.6 |
| 19743 | 14.3 | 39.7 | 1 | 0 | 8.6 |
| 22775 | 10.3 | 35.7 | 1 | 1 | 2.1 |
| 24229 | 0.9 | 26.7 | 1 | 0 | 9.6 |
| 41195 | 8.6 | 66.7 | 1 | 0 | 4 |
| 23143 | 4.2 | 63.1 | 0 | 1 | 10.6 |
| 13400 | 28.1 | 46.7 | 0 | 0 | 5.8 |
| 21371 | 14.9 | 78.9 | 1 | 0 | 7.4 |
| 28675 | 7.7 | 63.4 | 0 | 0 | 10.3 |
| 18171 | 6.9 | 47.9 | 0 | 1 | 6.3 |
| 23670 | 20.5 | 46.3 | 1 | 1 | 6.7 |
| 29745 | 6.1 | 53.9 | 1 | 0 | 6.7 |
| 14672 | 13.9 | 46 | 1 | 0 | 13.3 |
| 20382 | 0 | 38.6 | 1 | 1 | 4.1 |
| 24952 | 14.6 | 53.8 | 0 | 1 | 4.6 |
| 28878 | 7.4 | 12.2 | 1 | 1 | 2.7 |
| 24558 | 24.5 | 37 | 1 | 1 | 8 |
| 20447 | 0.9 | 27.4 | 1 | 1 | 4.2 |
| 27714 | 8.7 | 58.1 | 0 | 0 | 9 |
| 18116 | 3.5 | 47.5 | 1 | 1 | 7.7 |
Transcribed Image Text:Column 1, Wage:
Average employee wage
Column 2, Pct PT:
Percentage of part-time employees
Column 3, Pct U :
Percentage of unionized employees
Column 4: Av Shift : Availability of shiftwork
1= Yes
0 = No
Column 5: U/M Rel : Union-management relationship
1= good 0 = Not good
Column 6: Absent: Average number of days absent per employee
Transcribed Image Text:Perform a two-sample t-test analysis to determine if mean absenteeism is different between organizations
which have a good Union management and those that do not.
F-Test Two-Sample for Variances U/MO Absent U/M 1 Absent t-Test: Two-Sample Assuming Unequal Variances
Мean
7.972222222
5.253125
1
Variance
15.31920635
6.493640873
Mean
64
7.972222 5.253125
Observations
36
Variance 15.31921 6.493641
df
35
63
Observati
Hypothesi
36
64
F
2.359108957
df
52
P(F<=f) one-tail
0.001495541
t Stat
3.745595
F Critical one-tail
1.609102009
P(T<=t) or 0.000226
t Critical o 1.674689
P(T<=t) tw 0.000452
t Critical t 2.006647
Perform a similar analysis to determine if the availability of shiftwork significantly influences
absenteeism.
F-Test Two-Sample for Varlances
Av Shift No
Av Shift Yes
t-Test: Two-Sample Assuming Unequal Variances
Мean
5.306060606
6.688059701
Variance
10.03871212
11.39621891
Mean
5.306061 6.68806
Variance 10.03871 11.39622
Observations
33
67
Observati
33
67
df
32
66
Hypothesi
0.880880949
df
68
t Stat
-2.0067
P(F<=f) one-tail
0.353597115
P(Tct) or 0.02438
t Critical o 1.667572
P(Tc=t) tw 0.04876
t Critical t 1.995469
F Critical one-tail
0.586875931
Perform a stepwise regression to obtain the best multiple linear regression equation.
Regression Analysis:
The regression equation ia
Absent - 10.3 - 0.000203 Mage - 0.107 Pet PI + 0.0599 Pet U+ 1.56 Av Shift
- 2.64 UM Rel
edictor
Conatant
Wage
Pet PT
Pet U
Av Shift
SE Co
1.172
Coef
10.245
8.76 0.000
-0.00020330 0.00003573 -5.69 0.000
-0.10687
0.02949 -3.62 0.000
0.059es
1.5619
-2.6366
0.01240
0.5027 3.1i 0.002
4.03 0.000
UM Rel
0.4922 -5.36 0.000
S- 2.355e9 R-Sa - 53.21 R-Sq(ad) - 50.78
Analysis of Variance
Source
Regression
Residual Error 94
DF
55
MS
593.90 118.78 21.40 0.000
521.72
99 1115.62
5.55
Total
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