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.

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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 Av Shift to describe how organizations who offer shift work compare to those that do not.  (both MLR and t-test are provided below)

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
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
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: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
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