After conducting linear regression analysis initially on the relationship between %Food Insecurity and %Obesity and furthering our focus to investigate health disparities between urban and rural populations, I analyzed the data again, by performing linear regression specifically within the Urban-Rural indicator subset. The resulting R-squared value was very low at approximately 0.0025, indicating that the model offers very limited explanatory power for %Obesity based on %Food Insecurity within the Urban-Rural subset, which in my understanding implies that %Food Insecurity alone may not be a strong indicator of %Obesity in these areas, and other influential factors also play a significant role.
For a better understanding of the relationship between these variables, I also conducted a Pearson correlation analysis, revealing a correlation coefficient (r) of approximately 0.3538 and a high p-value.
After obtaining these results, the next step was to interpret their implications. After some research, I learned that the in results I obtained that is a positive value of r (0.3538) indicates a positive linear relationship, meaning that as one variable increases, the other tends to increase as well. While at the same time the high p-value indicates that the strength of this relationship is relatively weak (as null hypotheses gets rejected). Therefore, correlation does not mean causation.
With this I believe that additional factors beyond %Food Insecurity are at contributing to health disparities and more set of variables could influence %Obesity. I further plan to explore more variables and perform multiple regression on that dataset.