Management Analytics: Regression Analysis on Gender and Salary

This report presents a regression analysis on the association between gender and salary in management analytics. The descriptive statistics show that there is no link between gender and wage differential. The report recommends complying with legislation and conducting effective training and development sessions to improve female employees' performance.

Management Analytics

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
Descriptive analysis.....................................................................................................................3
Regression analysis......................................................................................................................4
Managerial interpretation and implications.................................................................................5
Recommendation.........................................................................................................................6
CONCLUSION................................................................................................................................7
REFERENCES................................................................................................................................8

INTRODUCTION
Management analytics involves developing a precise understanding regarding factors that
influence managerial decisions and designing data to support the managerial decision. The entire
study is based upon the fictitious data in which data regarding to gender, employee experience,
years of education, department and number supervised is mentioned. The report based upon the
research question i.e. Does there is a link between gender and wage differential? Further, with
the help of regression analysis, study will define the association between the variables and then
make recommendations which in turn assist managers or company to make effective results.
Rationale for choosing the research question: The reason behind choosing the research
question is such that there are many companies who did not pay good amount to women and
generally they have less pay as compared to men. This is considering the biggest concern
according to managerial perspective but also affected the goodwill. However, in the mid of 90’s
company did not pay enough amount to their female employees and that is why, the gender pay
gap increases which affect the brand image of a company as well. Also, in US the gender gap in
pay remain relatively stable from last 15 to 20 years but in 2020, women earned 84% of what
men earned either by full or part time working. Thus, by using relevant analytical tool, it will be
easy to determine the association between salary (dependent) and gender (independent)
variables.
Descriptive analysis
Emp
loye
e
Sal
ary
Years of
previous
experience
Years
employe
d
Years
educati
on
Gen
der
Depa
rtmen
t
Number
supervise
d
Mean 23.5
3982
7.39 4.608696
10.3260
9
5.06521
7
0.47
826
1
2.847
826 5.695652
Standard
Error
1.97
9057
1621
.75 0.549465
1.12075
5 0.41865
0.07
446
5
0.181
159 1.506786
Median 23.5
3654
1.5 4 8.5 5 0 3 3
Mode
#N/
A
#N/
A 6 6 4 0 4 2
Standard 13.4 1099 3.72665 7.60132 2.83942 0.50 1.228 10.21952

Deviation 2262 9.24 8 2
504
7 683
Sample
Variance
180.
1667
1.21
E+0
8 13.88792
57.7801
9
8.06231
9
0.25
507
2
1.509
662 104.4386
Kurtosis -1.2
0.88
2208 5.337315
-
0.73432
0.21498
8
-
2.08
455
-
1.453
46 10.60604
Skewness
-
4.1E
-17
1.07
8567 1.906153
0.60311
3
0.40594
5
0.09
000
1
-
0.448
69 3.376348
Range 45
4559
2 19 27 12 1 3 45
Minimum 1
2365
4 0 0 0 0 1 0
Maximu
m 46
6924
6 19 27 12 1 4 45
Sum 1081
1832
060 212 475 233 22 131 262
Count 46 46 46 46 46 46 46 46
Regression analysis
Null Hypothesis (H0): There is no statistical association between the mean values of salary and
gender.
Alternative hypothesis (H1): There is a statistical association between the mean values of
salary and gender.
SUMMARY
OUTPUT
Regression Statistics
Multiple R
0.2486
77
R Square
0.0618
4
Adjusted R
Square
0.0405
19
Standard
Error
10774.
1
Observation
s 46

ANOVA
df SS MS F
Significa
nce F
Regression 1 3.37E+08
3.37E
+08
2.900
334 0.095616
Residual 44 5.11E+09
1.16E
+08
Total 45 5.44E+09
Coeffic
ients
Standard
Error t Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
42417.
58 2199.255
19.28
726
4.33E
-23 37985.28
46849.
89
37985.2
8
46849.8
9
Gender
-
5415.8
6 3180.117
-
1.703
04
0.095
616 -11825
993.24
82 -11825
993.248
2
Managerial interpretation and implications
The descriptive statistic entails that average salary of the chosen employees is $39827.39.
In addition to this, minimum salary earned by the employees is $23654 and maximum is $69246.
However, the mean of the gender is male and majority of them are male as well. Further,
majority of the chosen employees have 6 years’ experience in their organizations and 50% of
them have 4 years’ experience. Also, average number of employees have 10 years of previous
experience which exhibits that majority of them have enough working experience to run a
business in smooth flow. Moreover, the average number of years of education is 5 whereas most
of them have 4 years’ experience. Apart from this, it has been also interpreted from the
descriptive table that 2 supervisors are repeated for an employee’s whereas average number of
supervisor is 6. Overall, it can be stated from the interpretation that earning of employees is high
and all have enough working experience which assist employees to working in an effective
manner within their respective companies.
The summary output table from regression analysis exhibits that there is lower
association between salary and gender because the value of R Square is 0.061. So, it can be
stated that there is no change in the salary while changes in gender. Also, the value of multiple R
entails that there is only 24% change in the amount of employees when gender fluctuates.
Through Anova table, it can be interpreted that there is no association between the mean
values of salary and gender because the value of P (0.09) is greater than 0.05 (standard criteria).

Therefore, alternative hypothesis is rejected over null. Choi, Choi and Lee (2021) also supported
that earlier there were a huge wage difference between male as well as female such that most of
the secondary research shows there female earn 19% less than male due to gender (as a
characteristics). However, with the changing era, there are further dimensions which has a direct
link between salary i.e. years of experience, education.
Apart from this, different laws had implemented which include Equal Pay Act in which
companies do not discriminate employees on the basis of gender. This in turn reflected that there
is no association between the gender and wage differential. Kolpashnikova, Zhou and Kan
(2020) critically evaluated that generally discrimination may come in a form of barriers to entry
into certain prestigious sub-field for women, in which they get less prestigious job as compared
to men along with lower paying. Nevertheless, such activity performed earlier, but by
implementation of laws and effective rules companies cannot pay low on the basis of gender (as
shown in the anova table). The overall discussion reflected that due to low association between
dependent and independent variables, managers have to make effective strategies to overcome
such problem and it have adverse implications over the business as well.
Recommendation
From the above results, it has been examined that there is no link between the salary of
employees with gender. If the relationship exists, then it creates negative impact because image
of a company affected. That is why, there are list of suggestion which helps manager to minimize
its adverse impact:
 It is suggested to the manager to comply with all the set rules and legislation by
government in order to decrease the wage differential on the basis of gender (Manesh and
et.al., 2020). This includes Equal Pay Act in which company does not pay lower amount
to their female employees as compared to men.
 It is also suggested to managers that they must reserve upper designation to female
employees which reflected positive impression over other junior members. The candidate
for high authority should be recruited by considering years of previous experience,
education and other criteria (Matteazzi and Scherer, 2020).
 Further, it is also recommended to the managers that they must conduct effective training
and development session within premises which in turn help to improve the female’s
performance. Through this, female employees can also support the company to attain the

set target goals in near future. So, this will make the female employees motivated towards
their work and according to their capability, company must appraise or promoted.
CONCLUSION
By summing up above report it has been concluded through the descriptive statistical
table that the mean of the employee’s wages is 39827.39 whereas majority of the employees
have 6 years of previous experience. Also, through regression tool it has been also summarized
that there is no relationship between salary and gender because 0.09 > 0.05 and as a result null
hypothesis is accepted. By using the recommendation enlisted above, manager also improve the
performance and minimize the negative impact of wage differences.

REFERENCES
Books and Journals
Choi, E. Y., Choi, S. H. and Lee, H., 2021. The association between job quality profiles and
work-life balance among female employees in Korea: A latent profile
analysis. International Journal of Environmental Research and Public Health. 18(4).
p.1672.
Kolpashnikova, K., Zhou, M. and Kan, M. Y., 2020. Country differences in the link between
gender-role attitudes and marital centrality: Evidence from 24 countries. International
journal of comparative sociology. 61(5). pp.291-309.
Manesh, S. N. and et.al., 2020. Spatial analysis of the gender wage gap in architecture, civil
engineering, and construction occupations in the United States. Journal of Management in
Engineering. 36(4). p.04020023.
Matteazzi, E. and Scherer, S., 2020. Gender Wage Gap and the Involvement of Partners in
Household Work. Work, Employment and Society, p.0950017020937936.