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.
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.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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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.
Question: Use the t-tests and the slope coefficient for U/M Rel to describe how the relationship between the union and the management affects absenteeism. (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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