Me and my teammate marked the commencement of our project journey. Building upon insights gleaned from earlier blog posts, we delved into exploratory data analysis and conducted fundamental statistical analyses to unravel the intricacies of our data’s structure and distribution.
A spotlight in our exploration was cast upon the Q-Q plot, specifically targeting the relationships between inactivity and diabetes. Extracting pertinent data from our common dataset, we meticulously crafted Q-Q plots for both ‘% DIABETIC’ and ‘% INACTIVE.’ These visualizations serve as windows into the normality of the data distributions, offering a nuanced understanding of their patterns.
Additionally, we ventured into the realm of linear regression, employing it as a tool to model the association between inactivity and diabetes. Transforming our data into numerical matrices, we embarked on fitting a linear regression model. The calculated R-squared value, standing at 0.1951, indicates that roughly 19.51% of the variability in ‘% DIABETIC’ can be elucidated by the linear relationship with ‘% INACTIVE.’
While this modest R-squared value suggests a partial explanatory power, it also signifies that our chosen predictor variable, inactivity, captures only a fraction of the diverse factors influencing diabetes percentages. This prompts a crucial realization – there exists untapped variability that requires exploration. The low R-squared value underscores the importance of considering additional factors or deploying more sophisticated models to enhance predictive accuracy.
Interpreting our findings necessitates a context-dependent lens. We acknowledge the potential complexities inherent in the relationship between variables, and we remain open to the possibility of unaccounted influences on diabetes prevalence. As we navigate this data landscape, our journey is not only about numbers; it’s about unraveling the layers of information that guide us toward a more comprehensive understanding of the factors shaping diabetes outcomes.