Monica was hired to analyze the salaries of the 685 employees of a company. The company's leadership wants to ensure that people of different races and genders are not paid differently for the same work. The first step in Monica's project is to create a data display that clearly shows these data points: - the median salary - the lowest salary - the highest salary A sample of the data is shown below. \( \$ 51,745 ; \$ 54,512 ; \$ 57,322 ; \$ 57,750 ; \$ 64,820 ; \$ 67,500 ; \$ 68,448 ; \$ 72,020 ; \$ 74,485 ; \$ 81,167 \)
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Beyond the Answer
To analyze the salary data and showcase the key points effectively, Monica could create a simple box plot. This visual representation will highlight the median salary clearly as the line within the box, while the ends of the box will illustrate the lower and upper quartiles. Additionally, whiskers can extend to the lowest and highest salaries, providing a complete snapshot of salary distribution and ensuring clarity in presenting any disparities across different employee groups. For real-world application, Monica could consider incorporating demographic breakdowns based on race and gender within her analysis. By stratifying the salary data this way, she can identify any discrepancies more effectively. This ensures that the leadership not only understands the overall trends but also can address specific gaps and make informed decisions about equity in compensation practices across the company.
