Analysis of Factors Affecting Income Level: Education, Experience and Previous Jobs

This report analyzes the impact of education, experience, and previous jobs on income level. Descriptive statistics, correlation analysis, and regression analysis were conducted to determine the relationship between the variables. The findings suggest that education and experience have a positive impact on income level, while the number of previous jobs has a weak correlation. The report provides recommendations for recruitment companies to develop effective hiring strategies.

Running head: STATISTICS
Statistics
Name of the Student:
Name of the University:
Author’s Note:

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Table of Contents
Introduction:................................................................................................................................................................................................2
Data Analysis:..............................................................................................................................................................................................2
Task 1:.....................................................................................................................................................................................................2
Graphs:.................................................................................................................................................................................................2
Descriptive Statistics:..........................................................................................................................................................................3
Task 2:.....................................................................................................................................................................................................5
Correlation Analysis:...........................................................................................................................................................................5
Task 3:.....................................................................................................................................................................................................6
Regression Analysis:...........................................................................................................................................................................6
Conclusion and Recommendation:..............................................................................................................................................................9
References:................................................................................................................................................................................................10

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Introduction:
A recruitment company is eager to construct a model of income level. The company trusts on several factors that are education,
experience and number of previous jobs. These factors are putting an impact on the individual income level.
The employees are the most prominent and crucial segment of a company as they help in establishment of the company. It is a
common fact that the amount of income is dependent upon educational qualification and experience. Educational performances in
school and college as well as previous experience effect the chance of getting jobs and getting higher income. Here, the factors of
educational qualifications are “Year of post-16 education” and “Income level”. The experience is regarded in terms of “Number of
previous jobs”.
It is known that the education has the strongest and most consistent independent relevance with the salary of a person. The
income amount strongly relies upon class of occupation. The class of occupation is also dependent upon previous educational
qualifications. Especially, the number of educational years received after 16 years is very crucial. In this time period, each person
receives higher studies. Educational qualification completely or partially explains the standard of living of manual and non-manual
workers. The reason of higher or lower standard of living is the amount of income (Ferrer-i-Carbonell 2005). The outcomes of the
report as per positive relationship would help the managers of recruitment company to make a proper hiring strategy.
Data Analysis:
Task 1:
The aim of research is to analyse the influence of years in education and number of previous jobs on the amount of income of
the undertaken people. A positive and significant association would make the research successful.
The data is tabulated in MS-Excel spreadsheet. Researcher has analysed the data with MS-Excel 16 software. The “Data
Analysis” Toolpak is used for analysing the data set. Researcher carried out different types of statistical operations such as
“Descriptive Statistics”, “Correlation Coefficients” and “Regression” with the help of data analysis tool.
Graphs:
Figure 1: The scatter plot of “Years of post-16 education” vs. “Income level”
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45.00
f(x) = 2.58948545861298 x + 8.48657718120805
R² = 0.577631416138855
Scatterplot of Years of post-16 education vs. Income level
Years of post-16 education
Income level £000's
(Levine et al. 1999)
In this graph, “Years of post-16 education” is considered as independent variable and therefore plotted in X-axis. “Income level
₤000’s” is considered as dependent variable and therefore plotted in Y-axis. Fitting of the scatter plot is moderate.
As per Gregorio et al. (2002), people should ensure their higher levels of education to sustain in a socio-economy and higher income
level. The current study, supports the fact.
Figure 2: The scatter plot of “Years of work experience” vs. “Income level”
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0.00
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45.00
f(x) = 2.21783625730994 x + 7.72953216374269
R² = 0.648382656068449
Scatterplot of Years of work experiance vs. Income level
Years of work experiance
Income level £000's

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In this graph, “Years of work experience” is considered as independent variable and therefore plotted in X-axis. “Income level
₤000’s” is considered as dependent variable and therefore plotted in Y-axis. Fitting of the scatter plot is moderate.
The number of years of experience makes people salient with perceived entitlement and that led to more salary to them (Danziger,
1980).
Figure 3: The scatter plot of “Years of post-16 education” vs. “Income level”
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0.00
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35.00
40.00
45.00
f(x) = 2.63392857142857 x + 18.1785714285714
R² = 0.149741554937643
Scatterplot of Number of Previous Jobs vs. Income level
Number of Previous Jobs
Income level ₤000's
In this graph, “Number of Previous Jobs” is considered as independent variable and therefore plotted in X-axis. “Income level ₤000’s”
is considered as dependent variable and therefore plotted in Y-axis. Fitting of the scatter plot is very weak.
As per Heyneman and Loxley (1983), the lower economic countries have lesser probability to achieve jobs. The probability of having
lesser number of previous jobs of the young generation is high (Clark, Georgellis and Sanfey 2012). The current study also supports
that fact.
Figure 4: Grouped bar-plot
1 2 3 4 5 6 7 8 9 10 11 12 13
0.00
5.00
10.00
15.00
20.00
25.00
30.00
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40.00 Bar Chart
Income level £000’s
Years of post-16
education
Years of work
experience
Number of previous
jobs
Sample number
Values
Descriptive Statistics:
Table 1: Table of descriptive statistics of two variables “Income level ₤000’s” and “Years of post-16 education”
Years of
post-16
education
Values
Row
Labels
Average of Income level
£000’s
Count of Income level
£000’s
Sum of Income level
£000’s
Max of Income level
£000’s
Min of Income level
£000’s
2.00 15 3 45 21 9
5.00 20.2 5 101 24 17
6.00 19 1 19 19 19
7.00 31.5 2 63 39 24
8.00 24 1 24 24 24
10.00 37 1 37 37 37
Grand
Total 22.23 13 289 39 9
(Oja, 1983)
The descriptive statistics of “Income level” with respect to “Years of post-16 education” indicates that-
 The average amount of Income level ₤000’s is highest for the participants whose number of years of post education is 10 (37
₤000’s) and lowest number of years of post education is 2 (15 ₤000’s).

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 The sum of amount of Income level is 101 ₤000’s (highest) and 19 ₤000’s (lowest) for the participants whose number of years
of post education is 5 and lowest number of years of post education is 6 respectively.
 The highest value of maximum amount of Income level is 39 units in ₤000’s whose number of years of post-16 education is 7
and the lowest value of maximum amount of Income level is 19 units in ₤000’s whose number of years of post-16 education is
6.
 The highest value of minimum amount of Income level is 37 units in ₤000’s whose number of years of post-16 education is 10
lowest value of minimum amount of Income level is 9 units in ₤000’s whose number of years of post-16 education is 2.
 The range of amount of Income level is highest for the participants of 7 years post-16 education (15 units in ₤000’s).
Table 2: The table of descriptive statistics of two variables “Income level ₤000’s” and “Years of work experience”
Years of
work
experience
Values
Row
Labels
Average of Income level
£000’s
Count of Income level
£000’s
Sum of Income level
£000’s
Max of Income level
£000’s
Min of Income level
£000’s
2.00 9 1 9 9 9
3.00 20 1 20 20 20
4.00 21.5 2 43 24 19
5.00 15 1 15 15 15
6.00 22 1 22 22 22
7.00 20.5 2 41 24 17
8.00 21 3 63 24 18
11.00 37 1 37 37 37
12.00 39 1 39 39 39
Grand
Total 22.23 13 289 39 9
The descriptive statistics of “Income level” with respect to “Years of work experience” displays that-
 The average amount of Income level ₤000’s is highest (39 ₤000’s) for the participants whose number of years of work
experience is 12 and lowest number of years of post education is 2 (9 ₤000’s).
 The sum of amount of Income level is 63 ₤000’s (highest) and 9 ₤000’s (lowest) for the participants whose number of years of
work experience is 8 and lowest number of years of work experience is 2 respectively.
 The highest value of maximum amount of Income level is 39 units in ₤000’s whose number of years of work experience is 12
and the lowest value of maximum amount of Income level is 9 units in ₤000’s whose number of years of work experience is 2.
 The lowest value of minimum amount of Income level is 9 units in ₤000’s whose number of years of work experience is 2 and
the highest value of minimum amount of Income level is 39 units in ₤000’s whose number of years of work experience is 12.
 The range of amount of Income level is highest for the participants of 7 years work experience (7 units in ₤000’s).
Table 3: The table of descriptive statistics of two variables “Income level ₤000’s” and “Number of previous jobs”
Number of
previous
jobs
Values
Row
Labels
Average of Income level
£000’s
Count of Income level
£000’s
Sum of Income level
£000’s
Max of Income level
£000’s
Min of Income level
£000’s
0.00 14.33 3 43 19 9
1.00 22.67 3 68 24 20
2.00 26.6 5 133 39 17
3.00 24 1 24 24 24
4.00 21 1 21 21 21
Grand
Total 22.23 13 289 39 9

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The descriptive statistics of “Income level” with respect to “Number of Previous Jobs” indicates that-
 The average amount of Income level ₤000’s is highest for the participants whose number of previous jobs is 2 (26.6 ₤000’s)
and lowest number of years of post education is 0 (14.33 ₤000’s).
 The sum of amount of Income level is 133 ₤000’s (highest) and 21 ₤000’s (lowest) for the participants whose number of
previous jobs is 2 and lowest number of previous jobs is 4 respectively.
 The highest value of maximum amount of Income level is 39 units in ₤000’s whose number of previous jobs is 2 and lowest
value of maximum amount of Income level is 19 units in ₤000’s whose number of previous jobs is 0.
 The lowest value of minimum amount of Income level is 9 units in ₤000’s whose number of previous jobs is 0 and the lowest
value of minimum amount of Income level is 24 units in ₤000’s whose number of previous jobs is 3.
 The range of amount of Income level is highest for the participants of whose number of previous jobs is 2 (22 units in ₤000’s).
Task 2:
Correlation Analysis:
Table 4: The table of correlation coefficients
Income level £000’s Years of work experience Number of previous jobs
Income level £000’s 1
Years of work experience 0.805222116 1
Number of previous jobs 0.38696454 0.451619776 1
(Triola 2006)
The Pearson’s correlation coefficient is calculated as –
r xy= sxy
sx s y
,
where,
sxy=covariance of two variables , sx=standard deviatio of first variable∧s y=standard deviation of second variable
The correlation coefficients between three chosen variables are-
 The correlation coefficient of “Income level ₤000’s” and “Years of work experience” is 0.805. Therefore, the correlation
between these two factors is strong and positive. That is, for the increase or decrease of years of work experience, the Income
level in ₤000’s also increases or decreases significantly.
 The correlation coefficient of “Income level ₤000’s” and “Number of previous jobs” is 0.387. Therefore, the correlation
between these two factors is weak and positive. That is, for the increase or decrease of number of previous jobs, the Income
level in ₤000’s also increases or decreases insignificantly.
 The correlation coefficient of “Years of work experience” and “Number of previous jobs” is 0.452. Therefore, the correlation
between these two factors is moderately strong and positive. That is, for the increase or decrease of years of work experience,
the number of previous jobs also increases or decreases significantly.
As per Summers and Wolfe (1977), the higher correlation between experience and income amount indirectly fulfils cost-optimization
(minimization) criteria of an organization. Rosenbaum and Popkin (1991) correlated in their previous research that past experience of
an employee enhances the satisfaction of higher authority and customers. It directly influences the promotion and remuneration of the

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employees. According to the Frieze, Olson and Russell (1991), the relation of wage and experience is very important from the
managerial and recruiting perspective.
Task 3:
Regression Analysis:
Table 4: Table of Multiple Regression Model
Hypotheses:
Null hypothesis (H0): The influence of years of post-16 education, years of work experience and number of previous jobs on Income
amount is statistically insignificant.
Alternative hypothesis (HA): The influence of years of post-16 education, years of work experience and number of previous jobs on
Income amount is statistically significant.
The linear regression model is –
y = β0 + ∑
i=1
n
¿¿ ¿ xi) = β0 + β1*x1 + β2*x2+…+ βn*xn
Here,
y = dependent variable, β0 = intercept, xi = independent variables, βi = slopes or coefficients of the dependent variables (i=1(1) n).
Here, the dependent variable is “Income level in £000’s”. The dependent variable is also known as “response” variable of the
regression model. The independent variables are “Years of post-16 education”, “Years of work experience” and “Number of previous
jobs”. The independent variables are known as “predictor” variable of the regression model.
The multiple linear regression model is-
Income level £000’s = 3.302678 + 1.63686* Years of post-16 education + 1.430328* Years of work experience + 0.577215* Number
of previous jobs.
The value of multiple r is 0.903383. Therefore, the correlation coefficient between dependent variable and independent variables is
strong and positive. The value of multiple R-square is 0.8161. Multiple R-square is known as “Coefficient of variation”. Therefore, the

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independent variables can explain 81.61% variability of the dependent variables. The association is therefore found to be very high.
The fitting of the “multiple regression model” is also established to be very good.
 The p-values of the regression model of the “Year of post-16 education” and “Income level £000’s” is 0.018807. It is less than
0.05. Therefore, the null hypothesis of insignificant association of these two factors is rejected at 5% level of significance.
 The p-values of the regression model of the “Year of work experience” and “Income level £000’s” is 0.021221. It is less than
0.05. Hence, the null hypothesis of insignificant association of these two factors is rejected at 5% level of significance.
 The p-values of the regression model of the “Number of previous jobs” and “Income level £000’s” is 0.611965. It is greater
than 0.05. Therefore, the null hypothesis of the insignificant association of two factors is accepted at 5% level of significance.
The slopes refer that-
 For the 1-year increment or decrement, the “Year of post-16 education” increases or decreases by 1.636856 (in £000’s).
 For the 1-year increment or decrement, the “Year of work experience” increases or decreases by 1.430329 (in £000’s).
 For the 1-year increment or decrement, the “Number of previous jobs” increases or decreases by 0.57721467 (in £000’s).
 It is observed that as per positive values of the slopes, it can be referred that all independent variables and dependent variable
has positive correlation. That is, if the value of any of the independent variable increases, then the value of dependent variable
also increases. Conversely, if the value of any of the dependent variable decreases, then the value of dependent variable also
decreases.
The ANOVA table of “multiple linear regression model” shows that the value of F-statistic with 12 degrees of freedom is
13.31326. The calculated p-value of the F-statistic is 0.001. The calculated p-value is less than level of significance that is 5%. Hence,
we can reject the null hypothesis of insignificant association between dependent variable and independent variables (Data 1988).
Conversely, the alternative hypothesis of significant association between dependent variable and independent variables is accepted. It
is 95% evident that the three predictors “Years of post-16 education”, “Years of work experience” and “Number of previous jobs”
have significant effect on Income amount of 13 people.
It could be inferred with the analysis of multiple regression model, that the three independent variables “Years of post-16
education”, “Years of work experience” and “Number of previous jobs” have statistical significant effect on the dependent variable
“Income amount in £000’s”.
Figure 5: Line fit plots of regression model of the variables “Years of post-16 education”, “Years of work experience” and “Number of previous jobs”.
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45.00
Years of post-16 education Line
Fit Plot
Income level
£000’s
Predicted Income
level £000’s
Years of post-16 education
Income level £000’s
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2.00
4.00
6.00
8.00
10.00
12.00
14.00
0.00
5.00
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45.00
Years of work experience Line
Fit Plot
Income level
£000’s
Predicted
Income level
£000’s
Years of work experience
Income level £000’s

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0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
0.00
5.00
10.00
15.00
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25.00
30.00
35.00
40.00
45.00
Number of previous jobs Line Fit Plot
Income level
£000’s
Predicted Income
level £000’s
Number of previous jobs
Income level £000’s
Figure 6: Normal probability plot of the regression model
3.84615384615385
11.5384615384615
19.2307692307692
26.9230769230769
34.6153846153846
42.3076923076922
50
57.6923076923077
65.3846153846155
73.0769230769231
80.7692307692309
88.4615384615385
96.153846153846
0
5
10
15
20
25
30
35
40
45
Normal Probability Plot
Sample Percentile
Income level £000’s
Figure 7: The comparative bar plot of Income level and predicted income level.
1 2 3 4 5 6 7 8 9 10 11 12 13
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00 Income Level vs. Predicted Income Level plot
Income level £000’s
Predicted Income
level £000’s
Sample number
Income Amount
Conclusion and Recommendation:
From the analysis, it could be interpreted that amount of income depends significantly on the chosen factors that are two types
of educational qualifications and previous job experience. The independent factors have statistically significant influence on the
dependent variable. It could be recommended that if a person has greater number of previous jobs and higher educational level in
terms of years of post-16 education, then that person would have a better chance of getting a higher salary. Number of years of post-16
education is the most significant factor that influences income level. Managers should emphasize on higher educational level in
context of salary of the employees (Garn et al. 1977).
As per scatterplots, correlations and multiple regression equation,
 The moderate significant relevance is found between income amount and years of work experience.
 The moderate significant relevance is found between income amount and years of post-16 education.
 The strong linear association of income amount and years of work experience is revealed in multiple regression model.
 The strong linear association of income amount and years of post-16 education is revealed in multiple regression model.
A higher rate of income amount of the employees can provide a company-
 Employees would find the job more interesting
 Employees would create a good relationship with management

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 Employees can work independently.
 They would have good relations with colleagues.
As a result of higher number of satisfied employees of the organization, the company would develop more. The positive and strong
correlations between “amount of income and years of work-experience” and “amount of income and years of post-16 education”
refer that with the increment of years of work-experience and years of post-16 education, the amount of income also increases.
Further, with the decrement of these two dependent factors, the amount of income also decreases.

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References:
Clark, A., Georgellis, Y. and Sanfey, P., 2012. Job satisfaction, wage changes, and quits: Evidence from Germany. In 35th
Anniversary Retrospective (pp. 499-525). Emerald Group Publishing Limited.
Danziger, S., 1980. Do working wives increase family income inequality?. The Journal of Human Resources, 15(3), pp.444-451.
Data, S. and Using Descriptive Statistics Bartz, A.E., 1988. Basic statistical concepts. New York: Macmillan. Devore, J., and Peck.
Ferrer-i-Carbonell, A., 2005. Income and well-being: an empirical analysis of the comparison income effect. Journal of Public
Economics, 89(5-6), pp.997-1019.
Frieze, I.H., Olson, J.E. and Russell, J., 1991. Attractiveness and income for men and women in management. Journal of Applied
Social Psychology, 21(13), pp.1039-1057.
Garn, S.M., Bailey, S.M., Cole, P.E. and Higgins, I.T., 1977. Level of education, level of income, and level of fatness in adults. The
American journal of clinical nutrition, 30(5), pp.721-725.
Gregorio, J.D. and Lee, J.W., 2002. Education and income inequality: new evidence from cross‐country data. Review of income and
wealth, 48(3), pp.395-416.
Heyneman, S.P. and Loxley, W.A., 1983. The effect of primary-school quality on academic achievement across twenty-nine high-and
low-income countries. American Journal of sociology, 88(6), pp.1162-1194.
Levine, D.M., Berenson, M.L., Stephan, D. and Lysell, D., 1999. Statistics for managers using Microsoft Excel (Vol. 660). Upper
Saddle River, NJ: Prentice Hall.
Oja, H., 1983. Descriptive statistics for multivariate distributions. Statistics & Probability Letters, 1(6), pp.327-332.
Rosenbaum, J.E. and Popkin, S.J., 1991. Employment and earnings of low-income blacks who move to middle-class suburbs. The
urban underclass, pp.342-356.
Summers, A.A. and Wolfe, B.L., 1977. Do schools make a difference?. The American Economic Review, pp.639-652.
Triola, M.F., 2006. Elementary statistics. Reading, MA: Pearson/Addison-Wesley.