Please respond to a minimum of two peers. Include in your response:
- Do you agree with the relationship as described by your peer?
- Can you think of other variables that might help explain the relationship between the two variables they chose?
- If no relationship was found, can you offer any reasons why not?
- Was the correlation they computed strong enough to use to predict one variable from the other?
Please be sure to validate your opinions and ideas with citations and references in APA format.
Peer Post 1
I choose these two topics because you need to have a job in order to pay for your mortgage. Based out the graph more people that have jobs do not own their home, they might rent or live with family. My Correlation number is .99 which would make this a positive relationship. As the number of persons with a job increases the number of homeowners increase as well. This information refutes my alternative. The alternative would be that not having a job would increase the number of homes owned which is not the truth in this case. I feel like this data is not strong enough or specific enough because lots of people have jobs but it could be a salary range or a household size which would give us more specific information and you would see a broader range in numbers, here you can barley see the difference because of how large the population number is. I can conclude that having a job can raise the number of homes owned because If you do not work than typically it is hard to pay for your home. From 2015-2019 it was thought that 67% of those living on their own owned their homes there were also people in the process of building their homes which is not taken in to consideration and the average number of persons in the work force was 66.4% those percentages are pretty similar (Quick facts Wisconsin, 2019).
Reference: Quick Facts, Wisconsin, July 1, 2019, United States Census Bureau, https://www.census.gov/quickfacts/WI
Peer Post 2
I chose to compare the number of 3-bedroom households in my area, with the annual household incomes of $50,000 to $74,999 to see the correlation between income and number of houses from the years 2015 through 2019. I believed that there would be a positive correlation between the two variables because, the more money households are bringing in, generally relates to larger houses.
After collecting my data and making my graph, I was able to see that there is no relationship between these two variables. With my data, I was able to see that while the number of 3-bedroom households increase, the average household income of $50,000 to $74,999 decreased. This shows that the households in my area, of Northbrook, IL, are living in more 3-bedroom houses and making more than $50,000 to $74,999 (US Census Bureau, n.d.).
The statistical information that I gathered refuted my alternate hypothesis that an increase in occupied 3-bedroom equaled an increase in the number of households making $50,000 to $74,999. I found that there is no relationship between these two variables, and I believe that is because the amount of household income I chose to use was too low to comfortably afford a 3-bedroom household in my area.
The value to knowing the correlation between these two variables is very important when looking in different areas to buy a home, and the average income in that area, that afford those houses. In some areas of Illinois, a household income of $50,000 to $74,999 can comfortably afford a 3-bedroom house, but in other, more densely populated areas, you may not be able to afford a house of that size.
If I were to use a different variable, as in a larger household income, I believe that I would have seen a positive correlation between the two variables. For example, if I were to use the household income data for the category $75,000 – $99,999, I believe that would have shown a strong positive correlation. This information can be used by the city to better understand the housing market, and why it is important to address the struggle of selling a home at a higher price.
I cannot conclude that one variable is causing the other, because that would mean that there is some type of correlation between the two, and there was no correlation at all between my two variables.
US Census Bureau. (n.d.-b). Data Profiles. Retrieved April 19, 2021, from https://www.census.gov/acs/www/data/data-tables-and-tools/data-profiles/2017/ (Links to an external site.)
Last Updated on April 23, 2021 by EssayPro