Data Analysis


The first graph shows the median earnings by sex and by educational status. I found this data table in the education section from the American Fact Finder website. The data was taken from the 2017 version in the Annandale, Virginia area (zip code 22003). When cleaning up the data I took out the grand total information, because I wanted to compare the data for men and women. The table listed five different average incomes for educational status; less than high school graduate, high school graduate, some college or associate’s degree, bachelor’s degree, and graduate or professional degree. No matter what degree is earned, men earned more money than women. However, the more education you have, the more money you will earn. That result holds for both men and women.

I chose to research data that compares males and females. Equal earning is the common debate over gender discrimination. It would be unfair if men still were paid more for the same job. I understand that people with more education background should get paid more than those who did not finish school. This data shows men are paid more than women, but lacks information on what job that they have.  As jobs differ a lot, obtaining this information is difficult.   I think elementary school teachers are similar enough so salaries for teachers can be compared if you have data on experience.

Data Analysis 1 – Sheet1


The second graph is based on educational degree held by males and females. The data I used was broken into two age groups: 18-24 and over 25 years. I also found this data table in the education section from the American Fact Finder website. I chose the same zip code area 22003 for Annandale, Virginia, because this is my hometown zip code. (I will probably be working in this area, so I wanted to do my research). When cleaning up the data I took out the data for the over 25 population that gives age information and over and the details on education below the high school level. To make the information clear, I did two separate charts, one for the population of 18 to 24 year olds and on for over 25. .

I wanted to compare males and females, this time for education background. I found the table very interesting. I liked how that used three variables; both, male, and female, to collect their data. It was interesting to find out that for the 18 to 24 year olds, the females have more higher education than males. But for over 25, more males have earned higher degrees. Maybe this education data will change as the 18-24 year olds age into the over 25 category. I think having the data in a chart makes it easier to understand than just reading it off a table.

Data Analysis 2 – Sheet1

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