Business Report: Analyzing Income Levels with Quantitative Methods

This report delves into the application of quantitative techniques in business, specifically focusing on the analysis of factors influencing income levels. The study examines the relationship between years of post-16 education, years of work experience, and the number of previous jobs, utilizing correlation and regression analysis to determine their impact on income. The report presents findings on the strength of these relationships, highlighting how increased education and work experience generally correlate with higher income levels. Additionally, it discusses the role of previous job experience and other factors such as skills and abilities, offering recommendations for individuals seeking to enhance their earning potential. The report concludes by emphasizing the utility of quantitative techniques in solving complex business problems and making informed managerial decisions.

Quantitative Techniques in
Business

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
Task 1...............................................................................................................................................3
TASK 2............................................................................................................................................4
Correlation Analysis....................................................................................................................4
Examination of strength of relationship between ratios..............................................................5
TASK 3............................................................................................................................................6
Regression Analysis.....................................................................................................................6
CONCLUSION................................................................................................................................7
RECOMMENDATION...................................................................................................................7
REFERENCE..................................................................................................................................9

INTRODUCTION
Quantitative technique and analysis is the process of visualizing and determining the
relation between the two factors which can be dependent on each other can also be not be
dependable. For this purpose, in process it is important to collect and evaluate the data and
information which can be measurable. It is also helpful for the businesses when they need to
their revenue, market share, wages of the employees in order to know and understand the
performance of the company. With the help of quantitative techniques correlation analysis and
the regression analysis, the company can also understand the relation between the experience of
the candidates and the income level of that candidates. It is a scientific method with the help of
which the businesses can also find the impact of the years of the post 16 education of the student
on their level of the income. With the help of the QT, the managers of the company can take the
managerial decisions. The report will also state the impact of the number of previous jobs on the
level of income of the individuals along with their graphical presentation.
MAIN BODY
Task 1
Analyses of relation between year of post-16 educations and income level:
This point A indicate that the if an individual have high year of post-16 educations, then
level of income of that individual is also increases with it (Basias and Pollalis, 2018). The reason
behind it is that they have more knowledge than the others and explore more technological area

as well. The Point B is indicating that the no. of years of education of an individual is less than
after their income in increasing. As per the Waibel and et.al. (2017), views it is because of the
difference between the quality of their education and also the level of the college from where
they complete their education such a Harvard and any local college. The Point C indicate that the
two individual with same years of education in which one is earning high income and one is
earning less income it may be because that one is only doing diploma course and one is
completing the full time collage course.
Analyses of the relation between year of work experience and income level:
The Point A indicate that with the increase in the year of job experience, the level of the
income of individual also increases. The reason for this may as per the Ford and Choi (2018), be
because high experience individual has high skill of critical thinking, presentation, analysis than
others. The Point B indicate that with the increase in the job experience, the level of income of
individual decreases. It may be because of the individual do not learn and explore anything
during their job. The Point C indicate that with the same year of experience, the both individual
have different level of income. The reason behind this may be that the individual with less
income have changed their field of job in order to explore new thing and now considered as
fresher.

Analysis of the relation between no. of previous jobs and level of income:
The Point A indicate that the individual with the entry level and 0 previous jobs is have
less income as compared to the mid-level and senior level candidates. As per the Kossek and
Lautsch (2018), opinion it is because the entry level candidate have only bookish knowledge not
practical knowledge and also they don't have any experience of job as well. The Point B
indicating that the individual with the senior level and 2 or more number previous jobs have high
income because of their expertise level is high. Along with that their experience is also high and
their quality of the work is much better compared to entry level candidates. The Point C indicate
that the individual with mid-level candidates have more knowledge but because of the low level
of experience that individual have low income as compared to the senior level candidates.
TASK 2
Correlation Analysis
Particulars Income
level
Years of post-16
education
Years of work
experience
Number
of
previous
jobs
Income level 1 0.760020668 0.805222116 0.386964
54
Years of post-16
education
0.76002066
8
1 0.515422114 0.515422
114
Years of work
experience
0.80522211
6
0.515422114 1 0.140782
211
Number of previous 0.38696454 0.140782211 0.451619776 1

jobs
Table 1: Correlation matrix
Examination of strength of relationship between ratios
Correlation analysis is the statistical tool to identify the strength of relationship exists between
factors. It can be mentioned as high, medium or low, where degree of correlation between 0.5 – 1
is considered to be a high correlation between two variables. High correlation signifies that when
there is a change in one variable, the another variable must be changed in the same proportion as
the former. When the resulting degree of correlation falls between 0.25 – 0.5, then it is
considered as the medium correlation between two variables which signifies that the resulting
change in the another variable would not be same as the former one. The last degree of
correlation is the indicator of weak relationship between two variables and the range of degree of
correlation is between 0 – 0.25. It signifies that a change in one variable would less likely to
cause a change in the second variable.
In the above matrix indicating the relationship between income level and many factors that can
have impact over the level of income, there are many conclusions that can be drawn as follows:
 While looking at the degree of correlation between income level and years of post-16
education, it is around 0.76, which falls in the range of high correlation and indicate that
a change in number of years of education post 16 will cause a considerable or high
change in the level of income.
 On looking at the degree of correlation between years of experience and income level, the
resulting degree is 0.81, which again indicates a strong relationship between experience
and level of income, so higher experience leads to higher level of income.
 Number of previous jobs and income level indicates a moderate degree of correlation
between them, which is around 0.39, which signifies that more jobs in past has very
impact on the current level of income.
According to Park and MERCADO (2018), there are many factors other than education,
experience and number of jobs performed previously which can affect the income of an
individual such as skills and abilities and even the economic trends existing in an economy such
as employment level, income level of an economy as a whole and supply of labour forces.

TASK 3
Regression Analysis
Regression Statistics
Multiple R 0.903382852
R Square 0.816100578
Adjusted R Square 0.754800771
Standard Error 4.038816153
Observations 13
ANOVA
Particulars df SS MS F Significance
F
Regression 3 651.499369 217.1664563 13.31326497 0.001169298
Residual 9 146.8083233 16.31203592
Total 12 798.3076923
Particulars Coefficients Standard
Error
t Stat P-
value
Lower
95%
Upper
95%
Lower
95.0%
Intercept 3.303 3.216 1.027 0.331 -3.974 10.579 -3.974
Years of post-
16 education
1.637 0.572 2.859 0.019 0.342 2.932 0.342
Years of work
experience
1.430 0.514 2.785 0.021 0.269 2.592 0.269
Number of
previous jobs
0.577 1.099 0.525 0.612 -1.908 3.062 -1.91
Regression analysis: It is a statistical tool used for the identification of the impact of one factor
over the other. It allows to recognise factors that matters and factors that can be ignored along
with how each of the factors under consideration influence each other.
Goodness of fit: For a statistical model like regression model, goodness of fit is the indicator of
suitability of model for a given set of observations. In regression, goodness of fit is measured
through R square and adjusted, which summarizes inconsistency between observed expected
values through the model.

R square: In case of multiple regression as here, the r square is called as the coefficient of
multiple determination and a higher value r square is considered to be better. Here it is 0.82,
which indicates that in dependent variable that is income level, 81% of the variation in it can be
expect from the variation in the dependent variable such as education, experience and number of
previous jobs.
Adjusted R: It is the modified version of r square which has been improved due to the inclusion
of number of predictors in the model. Here it is 0.75 which means there would be 75% change in
the expected variation in the dependent variable with inclusion of additional independent
variable.
Significance: A very low value of 0.00 less than the P-value of 0.05 at the 95% confidence level
indicates that there is significant relationship between dependent variable that is income level
and independent variables such as education, experience and number of previous jobs.
As per the views of Jebb and et. al. (2018), income level is directly related with the level of
education an individual hold where one can obtain higher pay through their knowledge. Also, an
educated individual with zero level of experience may not be able to get that much pay as obtain
by experienced individuals (Arafat, 2019). It has also been identified that many other factors play
an important role in determining the level of income such as previous experience in different
field and economic conditions prevailing in the country.
CONCLUSION
The report concludes the relation between the job experience, year of education, no. of
previous jobs with the level of income is both direct and indirect. The report also concludes that
the if individual wants to earn more, they have to focus on the other activities as well with the
bookish knowledge. The report also state that the QT also help the businesses in solving the
complex problems on less time and also with accuracy.
RECOMMENDATION
 With the help of the report it is recommended to the individual that besides bookish
knowledge, they also need to put their focus on the practical knowledge by joining
various courses.

 It is also recommended to the candidate that year of education do not affect their level of
income, if they gave good industrial knowledge and technological knowledge.
 As of now the technology is changes on a regular basis, so companies recommend those
candidates more who have more software knowledge. So it is recommended to the
individual that they must gain software and digital knowledge such as cloud computing,
ERP, CRM etc.
 It is also recommended to the students that in order to gain high pay job, they have to
complete their graduation and masters from a well-qualified college on an regular basis.
They have to make sure that they do not take any diploma degree to show them as a
graduate candidate.

REFERENCE
Books & Journals
Basias, N. and Pollalis, Y., 2018. Quantitative and qualitative research in business & technology:
Justifying a suitable research methodology. Review of Integrative Business and
Economics Research, 7, pp.91-105.
Waibel, S. and et.al., 2017. Career consequences of transnational educational mobility: A
systematic literature review. Educational Research Review, 20, pp.81-98.
Ford, K. and Choi, J., 2018. The Importance of Skills and Majors in Determining Future
Earnings. Retrieved October, 14, p.2019.
Kossek, E. E. and Lautsch, B. A., 2018. Work–life flexibility for whom? Occupational status and
work–life inequality in upper, middle, and lower level jobs. Academy of Management
Annals, 12(1), pp.5-36.
Vargas-Vera, M. and et.al., 2017. A E-Business Case of Study: Modelling the Quality of the
Wine using its Physicochemical and Qualitative Properties. International Journal of
Knowledge Society Research (IJKSR), 8(3), pp.1-20.
Jebb, A. T., and et. al., 2018. Happiness, income satiation and turning points around the world.
Nature Human Behaviour, 2(1), pp.33-38.
Park, C. Y. and MERCADO JR, R.O.G.E.L.I.O., 2018. Financial inclusion, poverty, and income
inequality. The Singapore Economic Review, 63(01), pp.185-206.