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BU1007 Business Data Analysis and Interpretation

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Business Data Analysis (BU1007)

Added on  2020-02-24

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TITLE PAGE
Student name
Student Id
continued over page
Case study for BU1007 Business Data Analysis and
Interpretation
TABLE OF CONTENTS
Introduction 3
Method 4
Results 4
Appendix 7
Conclusion 18
References 19
2
Case study for BU1007 Business Data Analysis and
Interpretation
INTRODUCTION
This report looks at the data provided to provide an insight into the
workplace gender relation using a host of parameters for the
University. These parameters include gender, salary, academic
degree, and academic division, nature of employment, and job role,
mode of transport used. All data is given campus wise as well.
This data can be used for multiple purposes. It can help to look at
gender neutrality across job role, academic qualifications,
experience level and salary. Neutrality implies that there must not
be inequality in salary(for example) among men and women. The
same applies t other parameters; we must have similar experience
in terms of number of years among both men and women.
This data can also be used to investigate differences across
campuses in chosen parameters.
We can also use this data to check the differences across campuses
in any chosen attribute. This helps to make the campuses more
equitable and equally attractive for employees.
The HR department can use the data constructively to frame
policies like retention policy and salary structures.
3
Case study for BU1007 Business Data Analysis and
Interpretation
METHOD:
We use statistical and visual aids to help us determine an overall
picture of employment scenario in the University. The general
characteristics of the University are shown with appropriate tools
like pie charts, bar charts, pivot table and contingency tables.
We make extensive use of Excel in this report to support the results.
RESULTS
We begin with salary as a first variable. The mean salary stands at
4109.33, while the mode is \$140. The median salary is \$105. This
shows a rather skewed picture of the salary, as shown by a negative
skewness measure at -0.144. the measure of dispersion used is
coefficient of variation, which equals 0.34. this is low and implies the
lack of outliers in the salary- there is no one with a very high or very
low salary. The interquartile range = 34.25. this range is an absolute
measure to judge dispersion. The use of CV which is a relative
measure gives a better picture. The other data descriptive statistics
are given in table 1 in Appendix. We also show a histogram for
salary in figure 2A. This shows that salary is mildly skewed in the
negative direction. This implies that more employees have higher
salaries, so that frequency of higher salaried employees is higher.
This is better than positive skewed salary structure that gives lower
salaries to more number of employees.
If we look campus wise we use table 2. This shows that average
salary is highest at BNE. Salary is mostly negatively skewed except
at TVSL. The dispersion is almost at 0.3 for all campuses except at
BNE where it is 0.11. This means that at BNE salary is highest in
average terms as well as most equally distributed. Both these facts
are laudable. The values of mode, median and mean are closest for
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