Business Statistics Report 2022

Running head: BUSINESS STATISTICS
Business Statistics
Name Of Student:
Name Of University:
Authors Note:

1
BUSINESS STATISTICS
Table of Contents
Introduction:...............................................................................................................................3
Identifying Data Types:..............................................................................................................3
Descriptive Statistics..................................................................................................................4
For numerical variables..........................................................................................................4
Numerical measure for categorical variable:.............................................................................6
Data summarising:.....................................................................................................................6
Frequency table for two different categorical variable:.........................................................7
Frequency table for two different numerical variable:...........................................................7
Two way frequency table for marital status and regions containing them:...........................9
Scatter plot of age and years of experience..........................................................................10
Scatter plot between age and salary:....................................................................................11
Discussion of the results and recommendations:.....................................................................12
References:...............................................................................................................................13

2
BUSINESS STATISTICS
Introduction:
The main goal of this study is estimating the job satisfaction of employees working
for a company. Job Satisfaction depends on many different factors from salary, the kind of
work and the overall environment of the company. There are some relation, in general
between, job satisfaction and happiness, years of experience and salary etc. For this report a
survey is conducted and various data relating to different categories were collected. The
values were of different data types and it has been categorized accordingly. Age, salary, years
of experience are taken and their statistic, such as mean, standard of deviation, is calculated.
Other variables are also considered to see any patterns linking them [1].
The data given is studied and graphs between variables that might indicate some
relationship between them are analysed. A questions might be answered from such
investigations, such as if there is a link between years of experience and job satisfaction, or
any links between age of an employee and job satisfaction or any variables that might have a
relationship [2].
Many statistical tools to achieve our objective. The variables is at first classified into
their respective data types. This helps us understand which of the statistical tools such as
mean, median, mode or others we have to apply to the classes of data. The higher the data
level, the more sophisticated is our level of analysis. There are four levels of data
measurement: nominal, interval,ordinal and ratio [3]. Nominal data’s are the weakest forms
of data. For example in our case the marital status of the employees can have only two
results: 1. Married or 2. Single. Id, Gender, Status of promotion fall under this category.
Ordinal data’s are next in the hierarchy in the data measurement level. These are used when
there is a ordered relationship between the value assigned to the variables. Interval Data is the
set of data that has the ordinal properties but also the distance between the data can be

3
BUSINESS STATISTICS
measured. Temperature scales or time or happiness index are some examples of interval
scale. Data that have the properties of interval data but also have a absolute zero are called
ratio data.
Excel is used for most of our calculation in this report [4].
Identifying Data Types:
Table containing all names of variables and the associated data type as well as the
scale of measurement:
Names of variables Data Type
ID Qualitative
Gender Qualitative
Marital Status Qualitative
Age Quantitative
Years of experience Quantitative
City Area Qualitative
Region Qualitative
Departments Qualitative
Salary Quantitative
JSS before training Quantitative
JSS after training Quantitative
Life Happiness Score Quantitative
Promoted Qualitative

4
BUSINESS STATISTICS
Descriptive Statistics

5
BUSINESS STATISTICS
For numerical variables:
For age,
Mean= 41.88
Percentile
s Values
0.1 28.2
0.2 32
0.3 36
0.4 38.8
0.5 42
0.6 46
0.7 48
0.8 54
0.9 54
Median= 42
Variance = 103.2656
Std Dev. =10.16197
Range= 40
IQR = 18.

6
BUSINESS STATISTICS
For years of experience
Mean = 20.71
Percentile Values
0.1 5
0.2 12
0.3 15
0.4 20
0.5 23
0.6 25
0.7 27
0.8 29.6
0.9 32.9
Median=23
Variance= 90.67
Std Dev.= 9.52
Range= 35
IQR = 15

7
BUSINESS STATISTICS
For Salary,
Mean= 47.36
Percentile
s
Values
0.1 38
0.2 42
0.3 44
0.4 46
0.5 47
0.6 49
0.7 51
0.8 53
0.9 56
Median = 47
Variance= 44.44
Standard Deviation= 6.67
Range= 39
IQR = 8

8
BUSINESS STATISTICS
Numerical measure for categorical variable:
Data can be categorized into the levels of their measurement. Nominal, Interval,
Ordinal and Ratio in increasing order of priority for analysis of data.
Nominal categorical variables offer the lowest levels of analysis and all we can calculate is
the frequency for them. The codes attached to them have no particular significance as they
don’t have priority. For example, for the relationship status of a person, we may use 1=
Married, 2= Single, 3= Divorced and 4= others for collecting the data set. The code does not
have to be numeric. For such data we can calculate the frequency only. Ordinal categorical
variable rank higher up the level of measurement and we can calculate frequency tables or
two way frequency tables with them. At this level the data elements can be ordered on the
basis of a hierarchy among them, and values may be assigned indicating the order of
hierarchy. For example in our data set, the values for City where an employee is from, or
region where an employee is from, represents ordinal data.
Data summarising:
Frequency table for two different categorical variable:
We take the region coded east, west, north and south and calculate the frequency distribution
of people living in those regions.
FREQUENCY DISTRIBUTION OF REGIONS

9
BUSINESS STATISTICS
Region Frequency
East 71
North 47
South 59
West 123
Grand
Total
300
Next, the job satisfaction scores before training and the frequency distribution are calculated
in a tabular form:
FREQUENCY OF JSS SCORE BEFORE TRAINING:
JSS
SCORE
Frequency
1 93
2 224
3 207
4 96
5 10
Frequency of JSS score after training:
JSS score after
training
Frequency
1 8
2 31

10
BUSINESS STATISTICS
3 134
4 44
5 83
Grand Total 300
Frequency table for two different numerical variable:
Frequency table of salary received by the members.
Frequency table of Ages among the people.
Salary(in
thousands)
Frequen
cy
20-29 2
30-39 36
40-49 152
50-59 100
60-70 10
Grand Total 300

11
BUSINESS STATISTICS
Age
Freque
ncy
20-29 30
30-39 90
40-49 99
50-60 81
Grand
Total 300
20-29 30-39 40-49 50-60
0
20
40
60
80
100
120
Histogram of Age
Total
Two way frequency table for marital status and regions containing them:

12
BUSINESS STATISTICS
Count of
Region Region
Marital
Status East North South West
Grand
Total
Married 59 38 49 106 252
Single 12 9 10 17 48
Grand Total 71 47 59 123 300
Married Single
0
20
40
60
80
100
120
East
North
South
West
[6]
Scatter plot of age and years of experience:

13
BUSINESS STATISTICS
15 20 25 30 35 40 45 50 55 60 65
0
5
10
15
20
25
30
35
40
f(x) = 0.834141605077912 x − 14.2271837539963
R² = 0.792473246720041
Scatter Plot
Age
years Of Experience
From the scatterplot, we find the approximated line has the equation, y=0.8341 x−14.227
∴ R2=0.7925
Therefore, coefficient of co relation, R= 0.89.
In statistics, correlation coefficient r, is used measure the strength of the linear relationship
between the variables concerned [5]. The value of R lies between -1 and +1.
Our R= 0.89 signifies a strong positive correlation which indicates that with age years of
experience on the job increases.

14
BUSINESS STATISTICS
Scatter plot between age and salary:
15 20 25 30 35 40 45 50 55 60 65
0
10
20
30
40
50
60
70
f(x) = 0.0158478073369383 x + 46.696293828729
R² = 0.000583557298677873
Scatter Plot
Age
Salary
From the scatter plot the equation of the approximating line is y=0.0158 x + 46.696
And R2=0.0006
Therefore, co efficient of correlation, R = 0.02. It signifies there is a weak positive correlation
i.e a weak linear relationship exists between the two variables.
The scatter plot between Life happiness score and Job Satisfaction after training is studied to
find if there is a strong correlation as there is a general idea that higher job satisfactions
means more happiness.

15
BUSINESS STATISTICS
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5
0
2
4
6
8
10
12
f(x) = − 0.220325118185883 x + 5.45068533543864
R² = 0.00714061543746591
Chart Title
JSS Score
Happiness
The line of fit is y=−0.2203 x +5.4507
And, R = 0.08
Visually it becomes apparent that there is no strong relationship between job satisfaction after
training score and happiness index. This is graphically confirmed as R value indicates a weak
positive correlation. So it can’t be said that there is a strong relationship between job
satisfaction and happiness
Discussion of the results and recommendations:
Thus it can be seen that given a database that has a faithful collection of data can lead
meaningful investigation that reveals insights about the internal structure of an organization
[7]. There are many variables and we can calculate the statistics which we might think have

16
BUSINESS STATISTICS
an impact of the job satisfaction level of the employees. Here relationship between job
satisfactions score and happiness, age and salary, distribution of married and unmarried
employees are studied and seen if it has any relation with job satisfaction. From the frequency
distribution table of JSS before training and JSS after training it is clearly seen that after the
training the average satisfaction of employees increases [8].
References:
[1] D.F. Groebner, P.W. Shannon, P.C. Fry and K.D. Smith, . Business statistics. Pearson
Education UK, 2013.

17
BUSINESS STATISTICS
[2] C.A. Wong and H.K. Laschinger,. Authentic leadership, performance, and job
satisfaction: the mediating role of empowerment. Journal of advanced nursing, 69(4),
pp.947-959, 2013
[3] A.G Bluman, Elementary statistics: A step by step approach: A brief version (No. 519.5
B585E.). McGraw-Hill, 2013
[4] G. Davis and B. Pecar, Business statistics using Excel. Oxford University Press, 2013
[5] W.M. Mendenhall and T.L. Sincich,. Statistics for Engineering and the Sciences.
Chapman and Hall/CRC, 2016
[6] N. Yau, Data points: visualization that means something. John Wiley & Sons, 2013.
[7] K. McCusker, and S. Gunaydin, Research using qualitative, quantitative or mixed
methods and choice based on the research. Perfusion, 30(7), pp.537-542, 2015
[8] F.Jones, R.J. Burke, and M. Westman,. Work-life balance: A psychological perspective.
Psychology Press, 2013