Business Statistics Research 2022

Runniing head: BUSINESS STATISTICS
Business Statistics
Name of the Student
Name of the University
Course ID

1BUSINESS STATISTICS
Table of Contents
Introduction......................................................................................................................................................2
Identification of data types and measurement of scales..................................................................................2
Descriptive statistics.........................................................................................................................................3
Numerical measures for categorical variables..................................................................................................3
Methods of data summarizing..........................................................................................................................3
Relation between two numerical variables......................................................................................................7
Results and Discussion......................................................................................................................................8
Conclusion and Recommendation....................................................................................................................9
References......................................................................................................................................................10

2BUSINESS STATISTICS
Introduction
One of the primary challenges that businesses face today is the ensuring job satisfaction among the
employees. In order to address this issue HR of the company has decided to conduct a survey to identify
the level of job satisfaction of the employees. Employees’ satisfaction are measured into different period
before and after training. Data are collected to understand the impact of training on job satisfaction [1].
Another important objective of the research is to understand the relation of age to years of experience and
that of salaries earned.
In order to accomplish the research objectives, data are collected for 300 employees of the
business organization on various aspects such as gender, marital status, age, year of experience, city that
they come from, region that they come from, departments, salary, Job satisfaction score before and after
training, life happiness score and whether promoted or not. With the collected data different statistical
analysis has been done to carry out the research. The statistical analysis used for attaining the research
objectives include measures of central tendency, descriptive statistics, scatter plot, correlation coefficient
and such related measures.
Identification of data types and measurement of scales
Table 1: Variable name and measurement scale
Variables Scale
ID Identity
Gender Nominal Categorical
Marital status Nominal Categorical
Age Numerical
Years of experience Numerical
City that they come from Nominal Categorical
Region they come from Nominal Categorical
Department Nominal Categorical
Salary Numerical
Job satisfaction score before training Ordinal Categorical
Job satisfaction after training Ordinal Categorical
Life happiness score Ordinal Categorical
Promoted Nominal Categorical

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Descriptive statistics
The numerical variables in the data set include age, years of experience and salary [2]. Descriptive
statistics of the numerical variables are given in the table below.
Table 2: Descriptive statistics of age, experience and salary
Age Years of experience Salary(000)
Mean 41.88 Mean
20.7
1 Mean 47.36
Standard Error 0.59 Standard Error 0.55 Standard Error 0.39
Median 42 Median 23 Median 47
Mode 54 Mode 25 Mode 45
Standard Deviation 10.18 Standard Deviation 9.54 Standard Deviation 6.68
Sample Variance
103.6
1 Sample Variance
90.9
7 Sample Variance 44.59
Kurtosis -0.93 Kurtosis -0.95 Kurtosis 0.02
Skewness -0.16 Skewness -0.41 Skewness -0.10
Range 40 Range 35 Range 39
Minimum 20 Minimum 1 Minimum 26
Maximum 60 Maximum 36 Maximum 65
Sum 12564 Sum 6212 Sum
1420
8
Count 300 Count 300 Count 300
Largest(1) 60 Largest(1) 36 Largest(1) 65
Smallest(1) 20 Smallest(1) 1 Smallest(1) 26
Confidence
Level(95.0%) 1.16
Confidence
Level(95.0%) 1.08
Confidence
Level(95.0%) 0.76
First Quartile 32 First Quartile 13 First Quartile 44
Third Quartile 50 Third Quartile 28 Third Quartile 52
Interquartile Range 18 Interquartile Range 15 Interquartile Range 8
Numerical measures for categorical variables
Nominal categorical variables are expressed in the form of language description. There are no
numerical values associated with this kind of variable. They do have numerical value to indicate the
category but such values do not have mathematical meaning [3]. Computation of numerical measures for
these variables is meaningless. One can only compute frequency belonging to a particularly category.
The case of ordinal categorical variable however is different. In case where categorical variables can
be ordered, these are termed as ordinal variables. These kind of data has a combination of numerical and
categorical data. The data though have categories but the numbers associated with each order have
meaningful mathematical implication [4]. As the values are ordered according to scale taking numerical
measures such as average of the collected responses have definite meaning.
Methods of data summarizing

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Table 3: Frequency table for job satisfaction before training
Job Satisfaction Score before training(1-5) Total
1 93
2 112
3 69
4 24
5 2
Grand Total 300
Table 4: Frequency table for job satisfaction after training
Job Satisfaction Score after training(1-5)
Tota
l
1 8
2 31
3 134
4 44
5 83
Grand Total 300
1 2 3 4 5
0
20
40
60
80
100
120
Frequency of Job Satisfaction before training
Figure 1: Bar chart for frequency of job satisfaction before training

5BUSINESS STATISTICS
1 2 3 4 5
0
20
40
60
80
100
120
140
160
Job Satisfaction after training
Figure 2: Bar chart for frequency of job satisfaction after training
Table 5: Frequency table of age
Age Total
20-24 15
25-29 15
30-34 57
35-39 33
40-44 54
45-49 45
50-54 56
55-60 25
Grand Total 300
Table 6: Frequency table of salary
Salary(000) Total
26-30 2
31-35 11
36-40 30
41-45 75
46-50 82
51-55 67
56-60 27
61-65 6
Grand Total 300

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20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-60
0
10
20
30
40
50
60
Histogram of Age
Figure 3: Histogram of age
26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65
0
10
20
30
40
50
60
70
80
90
Histogram of Salary
Figure 4: Histogram of salary
Table 7: Two way table gender and department
Count of Departments Gender
Departments 1 2
Grand
Total
1 36 65 101
2 41 68 109
3 10 22 32
4 14 16 30
5 6 9 15
6 7 6 13
Grand Total 114 186 300

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1 2 3 4 5 6
0
10
20
30
40
50
60
70
80
Number of Males and Females by department
1
2
Figure 5: Number of males and females in different department
Relation between two numerical variables
15 20 25 30 35 40 45 50 55 60 65
0
5
10
15
20
25
30
35
40
Scatter Plot
Age
Years of Experience
Figure 6: Scatter plot age and year of experiences
Table 8: Correlation coefficient of age and years of experiences
Age
Years of
experience
Age 1
Years of
experience 0.89021 1
The correlation coefficient between age and year of experience is obtained as 0.89. The positive
correlation coefficient implies age has a positive association with year of experience. That means older
people in the organization are likely to be more experienced compared to younger ones [5]. The value of
obtained correlation coefficient is closer to 1 meaning that there exists a strong positive association
between age and year of experience.

8BUSINESS STATISTICS
15 20 25 30 35 40 45 50 55 60 65
0
10
20
30
40
50
60
70
Scatter plot
Age
Salary ('000)
Figure 7: Scatter plot between Age and Salary
Table 9: Correlation coefficient of Age and Salary
Age Salary(000)
Age 1
Salary(000)
0.02415
7 1
The computed correlation coefficient between Age and Salary is 0.024. Positive correlation
coefficient indicates the degree of association between Age and Salary is positive. That means with increase
in age salary is likely to increase and vice versa [6]. The estimated value of correlation coefficient is closer to
0 meaning that the degree of association between age and salary weak.
Results and Discussion
The main business issues considered in the report include impact of training on job satisfaction and
relation of age with years of experience and salary. The statistical analysis conducted in the previous
section help to understand the business issues considered in the paper.
Level of job satisfaction of the employees are measured on a scale of 1 to 5 where 1 indicates
extremely dissatisfied and 5 indicates extremely satisfied. The frequency table of job satisfaction before
and after training provide count of person expressing their level of job satisfaction. Before training most of
the employees in the organizations are dissatisfied with the job. Out of 300 employees sampled more than
200 employees are found to be dissatisfied. Only 2 employees are found to be satisfied before training.
The level of satisfaction among the employees are however changed after the training. Satisfaction
level increases after the training. After training program, more people have become satisfied with their job.
There remain only 39 dissatisfied people in the sample once the training has been conducted. More than

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125 people in the sample claims to be satisfied because of training. Training program thus has a positive
influence on improving level of satisfaction of people.
The relation between age and year of experience and that of salary can be understood from the
obtained correlation coefficient. The correlation coefficient between age and year of experience is obtained
as 0.89. The positive correlation coefficient implies age has a positive association with year of experience.
That means older people in the organization are likely to be more experienced compared to younger ones
[7]. The value of obtained correlation coefficient is closer to 1 meaning that there exists a strong positive
association between age and year of experience. The analysis of correlation between age and experience
thus shows a positive relation between age and experience. The older people spend a relatively larger time
in the organization which help them to gain more experience overtime.
The computed correlation coefficient between Age and Salary is 0.024. Positive correlation
coefficient indicates the degree of association between Age and Salary is positive. That means with increase
in age salary is likely to increase and vice versa. The estimated value of correlation coefficient is closer to 0
meaning that the degree of association between age and salary weak. The positive association between age
and salary suggest that older people are likely to earn more salaries compared to younger people. This is
because as age increases people become more skilled and experienced which help them to earn a higher
salary [8]. The relation however is weak meaning that there are different other factors other than age that
might influence salary of employees.
Conclusion and Recommendation
The analysis so far made found that job training program has a positive influence on increasing job
satisfaction of people. The result related to the association between age, experience and salary suggest
that age has a positive association with both year of experience and salary.
Ensuring satisfaction which is one of the biggest problem in most organizations today can be solved
by arranging training program. The company should focus on conducting suitable training program that can
increase satisfaction level among the employees. A satisfied worker tends to be more productive than a
dissatisfied one.

10BUSINESS STATISTICS
References
[1]. S, Siengthai, and P, Pila-Ngarm, August. The interaction effect of job redesign and job satisfaction on
employee performance. In Evidence-based HRM: a Global Forum for Empirical Scholarship (Vol. 4, No. 2, pp.
162-180). Emerald Group Publishing Limited, 2016
[2]. T.R., dos Santos, and L.E., Zárate, Categorical data clustering: What similarity measure to
recommend?. Expert Systems with Applications, 42(3), pp.1247-1260, 2015.
[3]. D.C., Brown, Models for Ordered and Unordered Categorical Variables. Population Research Center,
University of Texas, Austin, 2016.
[4]. M., Te Grotenhuis, B., Pelzer, R., Eisinga, R., Nieuwenhuis, A., Schmidt-Catran, and R., Konig, A novel
method for modelling interaction between categorical variables. International journal of public
health, 62(3), pp.427-431, 2017
[5]. D.R., Cox, Applied statistics-principles and examples. Routledge, 2018.
[6]. D.C., Howell, Fundamental statistics for the behavioral sciences. Nelson Education, 2016.
[7]. V.K., Rohatgi, and A.M.E., Saleh, An introduction to probability and statistics. John Wiley & Sons, 2015.
[8]. S., Boughorbel, F. Jarray, and M., El-Anbari, Optimal classifier for imbalanced data using Matthews
Correlation Coefficient metric. PloS one, 12(6), p.e0177678, 2017.