While the data is structured and lacks duplicate values, there are numerous other factors to consider when examining variables related to conditions like diabetes and obesity. For instance, if the weather in a particular county or state is excessively cold compared to others, people may increase their food consumption for survival. Furthermore, if a county is located within a state where fast food consumption is prevalent, the likelihood of individuals experiencing inactivity, obesity, and diabetes is significantly higher.
My strategy for tackling this project involves several steps. First, I plan to divide the counties based on their respective states and categorize them into either northern or southern regions. This division will provide valuable insights into why certain states or counties exhibit higher rates of diabetes, obesity, or inactivity.
My initial focus will be on inactivity and obesity, as I believe that inactivity often leads to obesity, which in turn can increase the risk of diabetes. To facilitate this analysis, I have organized the counties using the Federal Information Processing Standard (FIPS) codes, making it easier to group and study the data. Additionally, I have used Python to compute various statistical parameters such as mean, median, and standard variation to gain a deeper understanding of the data’s characteristics.
In conclusion, I plan to collaborate with Dr. Dylan George to determine the specific findings he requires from our data analysis. However, I have some uncertainty regarding the application of Heteroscedasticity using Python, and I intend to seek clarification from my instructors during class discussions.