For my final project, I chose to do option 2 with the data set being 1756. The data that this dataset includes is the quartering information of soldiers during the year of 1756. There are 12 columns across and 110 longitudinal columns. What each of the 12 latitudinal column describes goes as follows:
- Number: The first column is the number of home that is being described in the column across in the list of homes that quarter soldiers. This column ranges from 1 to 327
- Name: The second column is the name of the head of the household that is quartering soldiers in their home.
- Trade: The third column is the job that the head of the household has.
- Gender: The fourth column is the sex of the head of the household, being either male of female.
- Conv for officers: The fifth column is how many officers can fit in the house being described in the column across. This column only ranges from 1 officer to 2 officers.
- Officers upon a pinch: The sixth column represents how many officers can fit in the household, in a pinch. This number, for the most part, is higher than the conv for officers column, but not by much (usually just 1 higher).
- Conv for men: The seventh column is how many men can fit into the quartering home being described. The numbers in this column range from 2 men to 6 men.
- Men upon a pinch: This column is how many men can fit into the home being described, if absolutely necessary. This number ranges from 4 men to 10 men. This number, in every case, is higher than the previous column, conv for men,
- Number of fireplaces: This column is how many fire places that there are per household. The numbers range from 0 to 6.
- Rooms without fire: This column tells us how many rooms that there are in the home that don’t include fireplaces. The numbers range from 0 to 4.
- Rooms the family occupied: This column represents how many rooms in the house that are being occupied by family members.
- (no label): The last column is what the condition of the house is like. The options are none, good house, a very good house and rich, and rich.
The first relationship that I have found in the dataset that I have chosen is that most of the heads of the households that are willing to quarter soldiers are men, and when women did quarter soldiers, it was only men. Only 1 woman in the entire dataset was willing to quarter officers. Because of this, I’m going to research if there are and risks of women quartering soldiers vs. risks of women quartering officers. I’m also interested in fining the percent of households that had female heads of the household, versus the percent of households that were headed by men. I also want to find if there were any benefits for quartering soldiers during this time period.
The second relationship that I found in the dataset that I chose is that every single house that is listed as a good house, a very good house and rich, and rich are all headed by males. Due to this, I wonder what income for women was in that year, versus the average income for men for that same year. The only jobs that women are listed for having in this data set are Indian trader, muntua maker, mead house worker, and merchant. The list of jobs that are held by men during this time period include weapon maker, tavern keeper, taylor, silversmith, selling liquor, shop keeper, inn keeper,Indian trader, dram shop, brewer, britches maker, and black smith. Due to the short list of jobs that women had, I also am interested in finding out the types of jobs that men and women both had during that time period.
The third relationship in the dataset that I found is that many men were willing to quarter men, but not willing to quarter officers. Again, I’m interesting in finding out why this is.