Trusted by 2+ million users, 1000+ happy students everyday
Showing pages 1 to 4 of 18 pages
TITLE PAGE Student name Student Id continued over page
Case study for BU1007 Business Data Analysis and Interpretation TABLE OF CONTENTS Introduction3 Method4 Results4 Appendix7 Conclusion18 References19 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 4
Found this document preview useful?
You are reading a preview Upload your documents to download or Become a Desklib member to get accesss