Salary Dataset Based on Country and Race
In this project, the author sought to determine the relationship between an individual’s pay and other independent variables, including that person’s age, job type, work experience, education, ethnicity, gender, and the nation in which they reside.
The data-set used in this project is: LINK
AGE vs. Salary based on experience.
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Explanation: The provided scatter plot visually demonstrates a clear positive correlation between age and wage. Irrespective of the educational path pursued by individuals, there is a tendency for their incomes to increase as they age. Moreover, job experience exerts a positive influence on wage remuneration. It is evident that individuals with extensive employment experience tend to receive higher salaries. Moreover, a greater proportion of individuals are observed to successfully complete bachelor’s degrees, accumulate work experience, and attain higher salaries, in comparison to those who have obtained master’s and doctoral degrees.
Ethnic Background vs. Salary
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Explanation: The provided box plot illustrates the distribution of salaries among different ethnic backgrounds, as well as the disparities in wages between males, females, and those of other genders. Based on the box plot analysis, it can be observed that Asian and Black males exhibit the highest wage levels, while Hispanic and Mixed individuals tend to have comparatively lower earnings. When considering the gender pay gap, it is seen that Australian, Black, Chinese, Hispanic, and Korean females tend to have higher salaries, whereas Asian and Mixed females tend to have lower salaries. Moreover, across all ethnicity, there exists a conspicuous disparity in wages between females and males, with males consistently receiving higher incomes than their female counterparts.
Mission Animation!!
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Explanation: Regardless of the specific country, it is generally observed that as an individual’s job experience grows, their compensation tends to improve.