Quantitative Techniques in Business

This study explores the factors influencing income levels in organizations using quantitative techniques. It examines the impact of education, work experience, and previous jobs on income. Descriptive statistics, correlation analysis, and regression analysis are used to analyze the data. The findings reveal the significance of these factors and their implications for employee compensation. The study also highlights the use of quantitative tools for data analysis and decision-making.

Quantitative Techniques in Business

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
TASK 1: DESCRIPTIVE STATISTICS ........................................................................................3
TASK 2: CORRELATION ANALYSIS.........................................................................................4
TASK 3: REGRESSION ANALYSIS............................................................................................6
CONCLUSION AND RECOMMENDATIONS............................................................................7
REFERENCES..............................................................................................................................10

INTRODUCTION
In the current era, business unit places high level of emphasis on recruiting competent
and talented personnel for getting competitive edge. Business unit can get desired level of
outcome or success only when it has skilled and motivated workforce. In this regard, income is
recognized as one of the main variable which affects motivation level of employees. Thus, for
creating satisfied workforce recruitment firm wishes to know the factors that need to be
considered at the time of setting employee compensation or salary. The aim of current study is to
assess the factors which influence or impact income level of personnel within an organization.
The rationale behind conducting research is to ascertain specific factors which employer keep in
mind while setting pay for personnel. Moreover, there are numerous factors which closely
influences personnel income level include skills, experience, education, cost of living, cross
sector mobility etc. Hence, by employing regression, correlation and descriptive statistics tool
research could shed light on the factors that considered by the firm for deciding personnel’s
income level. In this, report will develop understanding about how several quantitative tools can
be used for data presentation, evaluation and analysis.
TASK 1: DESCRIPTIVE STATISTICS
Income
level
£000’s
Years of
post-16
education
Years of work
experience
Number of
previous
jobs
15 2 5 0
20 5 3 1
17 5 7 2
9 2 2 0
18 5 8 2
24 7 4 3
37 10 11 2
24 5 7 1
19 6 4 0
21 2 8 4
39 7 12 2
24 8 8 1
22 5 6 2

Particulars Income level
£000’s
Years of
post-16
education
Years of
work
experience
Number
of
previous
jobs
Mean 22.23 5.31 6.54 1.54
Standard
Error 2.26 0.66 0.82 0.33
Median 21 5 7 2
Mode 24 5 8 2
Standard
Deviation 8.16 2.39 2.96 1.20
Sample
Variance 66.53 5.73 8.77 1.44
Range 30 8 10 4
Minimum 9 2 2 0
Maximum 39 10 12 4
Sum 289 69 85 20
Count 13 13 13 13
Interpretation: In accordance with the above descriptive statistics, it has been analyzed
that the average of income level is 22.23 and years of work experience is 6.54. On the other side,
repetitive income level of client is £24000 and higher education is 5. Moreover, 50% of the
number of clients have 2 years of work experience. Millah, Maufidah and Komar (2019) also
supported that if an employee's income level is high it means that it have minimum work
experience of 2 years and good at higher education as well.
TASK 2: CORRELATION ANALYSIS
Income
level
£000’s
Years of
post-16
education
Years of
work
experience
Number
of
previous
jobs
Income 1 0.76 0.81 0.39

level
£000’s
Years of
post-16
education 0.76 1 0.52 0.14
Years of
work
experience 0.81 0.52 1 0.45
Number
of
previous
jobs 0.39 0.14 0.45 1
The above depicted table shows that income variable is highly associated with years of
post-16 education and work experience. Referring this, it can be presented that income level is
significantly based on educational aspects and working experience. Moreover, value of
correlation is greater than .75 which entails that high correlation takes place between income and
other concerned variables. This is because every job needs some form of education about their
particular job. …..also supported the same by stating that good education on a job gives a better
understanding and this also assists to deliver better work. Such that a good education brings
positive attitude towards work and also to increase the wealth, it is necessary to have a strong
educational background which in turn make individuals able to perform the work in a better
manner.
However, income and number of previous jobs moderately correlated with each other as
value accounts for .39 only. This in turn signifies that though an individual change their jobs
frequently but it does not cause positive impact upon their income level. That is why, there has
been a moderate level of relationship between both variables. As per the views of Boschman and
et.al., (2021) there have been many reasons of losing a job in previous years, but it does not
include in the work experience. Thus, income of an individual does not depend upon the
previous jobs, though it directly influences by the number of work experience and higher
education as well. Overall, the statement reflected that a good income level helps to increase the
status and thus work of a person shows the position and this is possible only if, the education
level of an individual is strong and also have a good work experience. Therefore, this assists to
provide a chance to earn living and improve the level of standards as well.

TASK 3: REGRESSION ANALYSIS
H0 (Null hypothesis): There is no significance difference in the mean value of income and other
independent variables.
H1: alternative hypothesis (H1): There is a significance difference in the mean value of income
and other independent variables.
Regression Statistics
Multiple R 0.903382852
R Square 0.816100578
Adjusted R Square 0.754800771
Standard Error 4.038816153
Observations 13
ANOVA
df SS MS F
Significance
F
Regression 3 651.499369 217.1665 13.31326 0.001169
Residual 9 146.8083233 16.31204
Total 12 798.3076923
Coeffici
ents
Standard
Error t Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
3.30267
7906
3.216494
161
1.026
794
0.331
322
-
3.9735
4
10.578
89
-
3.97354
10.578
89
Years of post-
16 education
1.63685
5045
0.572499
326
2.859
139
0.018
807
0.3417
72
2.9319
38
0.34177
2
2.9319
38
Years of
work
experience
1.43032
8185
0.513554
288
2.785
155
0.021
221
0.2685
88
2.5920
69
0.26858
8
2.5920
69
Number of
previous jobs
0.57721
4669
1.098502
388
0.525
456
0.611
965
-
1.9077
7 3.0622
-
1.90777 3.0622
By applying statistical analysis tool, it has found that value of R and R square implies
for .90 & .82 respectively. It shows that that income is having high relationship with other

variables taking into considerations such as experienced, number of jobs and education. Further,
as per r square value, income will be affected to a great extent if changes take place in the
education and experience level. ANOVA table clearly exhibits that P value derived is lower from
the standard figure (0.05). On the basis of this, P < 0.05, it can be entailed that alternative
hypothesis in true and other one false. Nevertheless, out of such 3 variables, previous job
numbers do not have significant impact on the income aspect.
This is also reflected that educational attainment and income are closely correlated with
higher degrees and this in turn also leads to increase salaries. As per the opinion of Falguera and
et.al., (2021) it is realized that higher education is quite important for people early in their
careers. On the other hand, work experience also contribute positively with respect to income
such that if an individual have a work experience of 3 years then they are able to learn new skills
and strong abilities to solve the problem immediately. This in turn assists employees to manage
the work and capabilities to complete the defined task accordingly. Therefore, with the help of
higher education, personnel have high chances to get high income and even there is a huge
earning gap between college graduates and those who have less education background.
The overall tool reflected that there is a strong relationship between income level with
other parameters and this in turn exhibit that the chances of earning is increases when other two
variables are taken into account such that education, work experience. Also, this in turn also
provide benefit to their employees in the terms of increasing skills, promotion etc. Or it can be
say that industries with higher education and training requirement tend to pay workers higher
wages. Also, the increased pay is due to their work experience and higher educational
background that direct affect the income level of an individual.
CONCLUSION AND RECOMMENDATIONS
By summing up this research, it can be concluded that working experience and education
level of personnel impact their salary level. It can be seen in the report by using descriptive
statistics that the average of income level of the raw data is £22000 whereas, job work
experience is 6.54. This in turn reflected that all client have a good experience and that is why,
have a good income level as well. On the other side, from the correlation analysis, it can be
summarized that there is a strong correlation between income level with work experience and

education level of personnel as the value is higher than 0.75. Whereas, there is a moderate
relationship between income and number of previous jobs as the value is in between 0.25 to 0.75.
Further, by applying the regression analysis, it is stated that there is a strong relationship between
independent (work experience, education, previous job) factors with dependent (income) level.
Therefore, the alternative hypothesis is accepted over other and that is why, if there is a
fluctuation in any independent factors, then it causes direct impact upon income of personnel.
Recommendations
 It is to be recommended to the company to hire those employees who have strong
competencies in order to deal with complex situation. This assists to manage complex
project and it also causes positive impact upon the performance of employees. Thus, it is
not necessary to always recruit higher educational candidates, but they must consider
whether employees have a strong capability that is suitable for the job position.
 It is also recommended to the company to include other parameters as well while making
any decision regarding employees such that skills, their efforts regarding any project.
These are also considered while deciding salary and this in turn helps to increase
employee motivation level as well. Therefore, it is necessary for the company to consider
such parameters as well that assists to make better decision for the welfare of an
employee.

REFERENCES
Books and Journals
Boschman, S. and et.al., 2021. From Social Assistance to Self-Sufficiency: Low Income Work
as a Stepping Stone. European Sociological Review.
Falguera, C.C. and et.al., 2021. Relationship between nurse practice environment and work
outcomes: A survey study in the Philippines. International journal of nursing
practice. 27(1). p.e12873.
Millah, H., Maufidah, H. and Komar, A., 2019. An Analysis of Affecting Factors for The
Community Income Levels around Steam Fired Power Plant (PLTU) Paiton. Indonesian
Journal of Islamic Economics and Finance. 2(2). pp.63-68.