Quantitative Analysis of Income and Business Factors Report

This report utilizes quantitative techniques to analyze the factors influencing income levels within a business context. It examines the impact of education, work experience, and the number of previous jobs on an individual's income, using a dataset of thirteen observations. The analysis includes graphical representations, correlation analysis to identify relationships between variables, and regression analysis to model the impact of independent variables on income. The findings lead to conclusions about the significance of educational achievements and practical skills in enhancing earning potential, with recommendations for individuals to prioritize education and training. The report also acknowledges that income is affected by other factors like skills and abilities. The report provides valuable insights for managerial decision-making, offering a comprehensive overview of income determinants within a business setting.

QUANTITATIVE TECHNIQUES IN
BUSINESS

TABLE OF CONTENTS
Introduction
Graphical representation and analysis
Correlation analysis
Regression analysis
Conclusion
Recommendations
References

INTRODUCTION
Quantitative techniques used for business purpose in managerial decision making
to identify lacking and leading factors along with dependency of various factors
associated with business on each other.
In this presentation, by taking income level of an individual as a dependent
variable, the impact of various independent variables such as education, experience
and number of previously performed jobs has analyzed through graphical
representation, correlation analysis and regression analysis.
The analysis is done on thirteen number of observations.

GRAPHICAL REPRESENTATION
AND ANALYSIS
1 2 3 4 5 6 7 8 9 10 11 12 13
15
20
17
9
18
24
37
24
19 21
39
24 22
2
5 5
2
5 7
10
5 6
2
7 8
5
Income level and Years of
post-16 education
Series1 Series2 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3
0
5
10
15
20
25
30
35
40
45
15
20
17
9
18
24
37
24
19 21
39
24 22
5 3
7
2
8
4
11
7
4
8
12
8 6
Years of work experience
and income level
Income level Years of work experience
1 2 3 4 5 6 7 8 9 10 11 12 13
0
5
10
15
20
25
30
35
40
45
15
20
17
9
18
24
37
24
19 21
39
24 22
0 1 2 0 2 3 2 1 0
4 2 1 2
Number of previous jobs and
income level
Income level Number of previous jobs

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 0.3869645
4
Years of post-16
education
0.76002066
8
1 0.515422114 0.5154221
14
Years of work
experience
0.80522211
6
0.515422114 1 0.1407822
11
Number of previous
jobs
0.38696454 0.140782211 0.451619776 1

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

CONCLUSION
From this presentation, it can be concluded that income level can be affected by
many factors such as education, experience level and how many jobs an individual
has undertaken previously.
 But the income level does not solely depends upon these factors as there are
several other factors too which can have an impact over the level of income such as
skills and abilities of an individual, etc.

RECOMMENDATIONS
It can be recommended through the analysis so done here as follows:
 an individual should always gave first priority for educational achievements in
order to enhance their earning level.
One must give priorities for skills learning and practical learning through trainings
along with theoretical knowledge to enhance their earnings.

REFERENCES
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.
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.