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• Learn to structure, clean, and manipulate datasets for analysis in Excel.
• Apply statistical functions, pivot tables, and data analysis tools to extract meaningful insights.
• Create charts and graphs to visualize data, and present findings clearly and professionally.
Module 1: Introduction to Reporting in Excel
This “Employee’s Annual Salary” dataset consists of 2000 employees and includes quantitative and qualitative variables related to employee salary, performance, and workplace characteristics. The dataset is structured as follows:
| Attribute | Description |
|---|---|
| Gender | Employee gender (Male, Female) |
| Job Role | Type of job (e.g., Manager, Developer, HR Specialist) |
| Department | Business function (e.g., IT, Sales, HR) |
| Salary | Employee’s annual salary |
| Years of Experience | Total years of work experience |
| Education Level | Employee’s highest level of education (Diploma, Bachelor’s, Master’s, PhD) |
| Projects Completed | Number of projects completed by the employee |
| Work Mode | Work arrangement (Remote, On-site, Hybrid) |
| Work Hours Per Week | Average weekly working hours |
| Performance Rating | Employee’s performance rating (scale of 1 to 5) |
Ensure that you start your analysis with the data cleaning process.
Using the provided “Employee’s Annual Salary” dataset, create a Pivot Table for each of the following:
1. Summarize sum salary by “Gender”.
2. Compare average performance ratings by “work mode”.
3. Summarize standard deviation for years of experience by “education level”.
4. Group the employees by “Job Role”.
Write a short summary for each table explaining the key insights from the data.
Prepare the following charts for each of the given categories:
1. Prepare a pie chart for “Job Role”.
2. Prepare a clustered bar chart for “Job Role” and “Salary”.
3. Prepare a scatter plot for “Work Hours Per Week” and “Salary”.
Write a short summary for each chart explaining the key insights from the data.
Perform an independent samples t-test to determine whether there is a significant difference in average “Salary” based on “Gender”. Ensure that you check the assumption of equal variances, report the t-statistic and p-value, and provide a brief interpretation of the results.
Perform a regression analysis to examine the relationship between “Years of Experience” and “Salary.” Interpret the regression results by analysing the coefficients, the significance of the findings, and the overall fit of the model. Additionally, discuss any limitations of the model and its practical implications.
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