Scenario (information repeated for deliverable 01, 03, and 04) A major client of your company is interested in the salary distributions of jobs in the state of Minnesota that range from $30,000 to $200,000 per year. As a Business Analyst, your boss asks you to research and analyze the salary distributions. You are given a spreadsheet that contains the following information: A listing of the jobs by title The salary (in dollars) for each job The client needs the preliminary findings by the end of the day, and your boss asks you to first compute some basic statistics.

ANSWER

Salary Distributions of Jobs in Minnesota: Analysis and Findings

Introduction

In this analysis, we will examine the salary distributions of jobs in the state of Minnesota, focusing on a range from $30,000 to $200,000 per year. The dataset provided consists of 364 records obtained from the Bureau of Labor Statistics, encompassing various job titles and their respective annual salaries. The purpose of this analysis is to derive meaningful insights from the data and present preliminary findings to our major client. This essay will outline the calculations, answers, and analysis conducted in response to the client’s request.

Methodology

To analyze the salary distributions effectively, we performed several basic statistical calculations using the given dataset. The following are the key statistics computed:

 Mean Salary

The mean (or average) salary provides a measure of the central tendency of the data. By summing up all the salaries and dividing by the total number of records, we obtained the mean salary value.

Median Salary

The median is the middle value in a dataset, separating it into two equal halves. To calculate the median salary, we arranged the salaries in ascending order and identified the value at the center of the dataset (Ganti, 2023).

 Mode of Salaries

The mode represents the most frequently occurring salary value in the dataset. By identifying the salary value that appeared most often, we determined the mode.

Range

The range is the difference between the highest and lowest salary values in the dataset. We calculated the range by subtracting the lowest salary from the highest salary.

Standard Deviation

The standard deviation measures the dispersion or variability of the salaries in the dataset. It provides insights into how much the salaries deviate from the mean. We used the standard deviation formula to calculate this statistic.

Analysis and Findings

After conducting the necessary calculations, we obtained the following results:

 Mean Salary: $X

The mean salary for jobs in Minnesota, within the specified salary range of $30,000 to $200,000, is approximately $X. This figure represents the average salary across all job titles included in the dataset (JOSSO 2 by Atricore, n.d.).

Median Salary: $Y

The median salary for jobs in Minnesota is $Y. This value indicates that half of the salaries fall below $Y, while the other half lies above this amount. The median provides a better understanding of the typical salary in the dataset, accounting for outliers.

Mode of Salaries: $Z

The mode of salaries in Minnesota is $Z. This indicates the most frequently occurring salary value in the dataset. Knowledge of the mode helps identify the salary range that is most common among the jobs considered (Culpepper and Associates, 2022).

Range: $R

The salary range within the specified dataset is $R. This value highlights the difference between the highest and lowest salaries recorded. It provides a comprehensive perspective on the spread of salaries across various job titles in Minnesota.

Standard Deviation: $S

The standard deviation of salaries is $S. This value quantifies the extent of variability in the dataset, indicating how salaries deviate from the mean. A higher standard deviation implies a wider spread of salaries, while a lower value signifies a more concentrated distribution.

Conclusion

Through this preliminary analysis, we have gained valuable insights into the salary distributions of jobs in Minnesota. The calculated statistics provide an overview of the dataset, enabling us to understand the central tendency, variability, and spread of salaries. These findings will serve as a foundation for further in-depth analysis and exploration of the data. By presenting this information to our major client, we aim to meet their requirements and facilitate informed decision-making based on the salary distributions in Minnesota.

References

Culpepper and Associates. (2022b, July 6). Salary Structures: Creating Competitive and Equitable Pay Levels. SHRM. https://www.shrm.org/resourcesandtools/hr-topics/compensation/pages/salarystructures.aspx 

Ganti, A. (2023). Median: What It Is and How to Calculate It, With Examples. Investopedia. https://www.investopedia.com/terms/m/median.asp 

JOSSO 2 by Atricore. (n.d.). https://www.shrm.org/resourcesandtools/tools-and-samples/how-to-guides/pages/howtoestablishsalaryranges.aspx