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 relationsh
Joanna For this discussion, I have chosen from the categories of economic characteristics employment status and housing characteristics housing tenure. The two variables specifically I have chosen are employed and owner-occupied housing, to see if there is a relationship between being employed and home ownership. The area the data is for is Grant Township, a subdivision of Lake County Illinois for 2013 through 2018. I hypothesize that there will be a positive relationship between the number of individuals employed and the number of owner-occupied housing. The scatterplot is as follows: The correlation coefficient is -.0369, a negative number close to zero. A correlation coefficient of zero, or close to it, means that for every increase, there isn’t a positive or negative increase (Glen, 2021). This concludes that there is no detectable relationship between the two variables I have chosen. My hypothesis that a relationship exists between the two has been refuted by the data. The reason I think I did not find a relationship is that many other factors may affect home ownership in addition to being employed. For example, when people get married, they normally would no longer have separate homes, but share one home. The number of people employed includes those aged 16 and up, who are legally eligible to work, therefore, if the increased employment numbers included school-aged teens or young adults, they are not likely to own their own homes, but rather still live with their parents or guardians.   The township may use the correlation between variables to determine how to best serve the community with businesses looking to build on available land or make improvements to high-traffic areas that bring in revenue for the city. According to Glen (2021), the Pearson correlation is not able to tell the difference between dependent and independent variables. . I don’t think there can be a definitive conclusion that one variable causes the other, only that there is a relationship as often there are multiple variables that can contribute to the outcome of another.
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 relationsh
Mackenzie , For my two variables, I chose the number of people who have a bachelor’s degree between 2014 through 2019 and the number of people whose income was between $50,000 – $74,999 between 2014 through 2019.  For this situation, the correlation coefficient was positive at 0.105 between the two variables. Something I thought was interesting is that for some of the time, whenever one of the variables would go up or down, the other remaining variable would follow. This correlation is positive.  The benefits of this information would be to show the connection between those people who have obtained a bachelor’s degree earning higher incomes.  With my findings, I am finding it hard to either accept or reject my null hypothesis because I didn’t find my information to be super consistent. I do think that there is an overall weak relationship between the two variables.