Instructions Scenario Information Repeated For Deliverable 0

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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. The data set in the spreadsheet consists of 364 records from the Bureau of Labor Statistics. It contains job titles with yearly salaries ranging from approximately $30,000 to $200,000 for the state of Minnesota. Your boss wants you to submit the spreadsheet with the completed calculations, and your research and analysis should be present within the answers provided on the worksheet.

Paper For Above instruction

Analyzing salary distributions within a specific geographic and salary range provides valuable insights for clients, especially when tailored to regional labor markets such as Minnesota. As a Business Analyst, the urgent task is to perform a preliminary statistical analysis of a dataset comprising 364 records from the Bureau of Labor Statistics. This analysis focuses on jobs with salaries between $30,000 and $200,000 per year and aims to produce key descriptive statistics to inform the client’s decision-making process.

The dataset includes diverse job titles and their respective annual salaries, which range broadly from approximately $30,000 to $200,000, reflecting the diversity of employment opportunities within Minnesota's economy. To conduct a meaningful analysis, the initial step involves filtering the dataset to include only those jobs falling within the specified salary range. This filtering ensures that the analysis remains relevant to the client’s inquiry.

Once the relevant data subset is isolated, calculation of fundamental statistical measures is necessary. These include measures of central tendency such as the mean and median, which offer insights into the average salary and the middle point of the salary distribution. The mode, if applicable, would identify the most common salary value among the jobs within this range. Measures of dispersion, such as the standard deviation and interquartile range, help understand the variability and spread of salaries, indicating how salaries differ across various jobs.

In addition to these basic statistics, identifying the minimum and maximum salaries within the dataset provides context for the salary spectrum. The range, calculated as the difference between the maximum and minimum salaries, indicates the extent of salary variation. For a more detailed view of the distribution shape, constructing a histogram or frequency distribution table can be very helpful. This allows visualization of how salaries are spread across different intervals and whether the data is skewed.

The analysis should also include the calculation of percentile values, such as the 25th, 50th (median), and 75th percentiles. These percentiles offer insights into the salary distribution's quartiles, highlighting the spread in the middle 50% of salaries and identifying potential outliers or unusual values.

It is essential to present these statistics in a clear, concise manner within the spreadsheet, with highlighted cells or notes that explain the significance of each measure. Including visual aids like charts or graphs can enhance the comprehensibility of the findings, especially for stakeholders who prefer visual data interpretation.

Given the limited time frame—an end-of-day deadline—an efficient approach involves using built-in spreadsheet functions such as AVERAGE, MEDIAN, MODE, STDEV, MIN, MAX, and QUARTILE to expedite calculations. Data filtering tools should be employed to restrict the dataset to salaries within the specified range before performing the calculations.

In conclusion, the preliminary analysis focused on basic descriptive statistics will provide the client with valuable insights into salary distributions across Minnesota jobs within the specified range. The completed spreadsheet with these calculations will serve as the basis for further detailed analysis and decision-making, highlighting the importance of accurate data handling and clear presentation.

References

• U.S. Bureau of Labor Statistics. (2023). Occupational Employment and Wage Statistics. https://www.bls.gov/oes/
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