Combined Data Sheets¶
We present a series of combinations of the previous datasets which will help us connect these three data groupings. First will be Job+Spouse, then Race+Job, and finally Race+Spouse.
Job+Spouse
Job+Race
Race+Spouse
So again now that the data is clearly presented it can be more easily quantified and understood¶
Job+Spouse
- We see in the data visualization that of those with Jobs (11697 data-points) 5399 also have Spouses, so **46% of those points which have jobs also are married**. Of those that are without Jobs (4013) only 470 are listed as having Spouses, so **12% of people without Jobs are listed as having Spouses**.
- This leaves the remaining 53.84 data points with Jobs without Spouses and **88% of those without Jobs as also not having Spouses**
- Looking first at the Black employment numbers, we see that of the 4593 number of data entries labled Black 3188 of them also have Jobs. So (3188/4593)100= **69% of the data entires labled Black have Jobs and the remaining 31% do not.**
- In the White data entires we see that of the 11117 entires 8509 have jobs. From this we can say that, (8509/11117)*100, **77% of White data entries have jobs and the remaining 23% do not.**
- Starting then with those who have a Spouse, of the 5869 of those with a Spouse 1684 of them are Black. So, (1684/5869)*100 =**29% of those with Spouses are Black**, this leaves the remaining **71% of those with Spouses as White**.
- Of those without a Spouse 2909 out of 9841 are Black, so, (2909/9841)*100 = **29.5% of those without Spouses are Black** and the **remaining 70.5 are White**.
- Caculating by percentage between Race and Spouses we see for White, (4185/11117)*100= **38% of White lables have Spouses**, for Black, (1684/4593)*100= **37% of Black lables have Spouses**.
Next: Conclusions