The analysis of CDC data on diabetes, with predictor variables being obesity and inactivity, revealed several key findings. Firstly, when examining the p-value of the data, it was found to be statistically significant, indicating a strong relationship between the predictor variables (obesity and inactivity) and the occurrence of diabetes. This suggests that these factors play a crucial role in the development of diabetes within the population studied. However, it’s important to note that the data exhibited homoscedasticity, which can make predictive modeling inefficient. This suggests that there might be some limitations when trying to accurately predict diabetes using obesity and inactivity as predictor variables. Homoscedasticity implies that the variance of the errors in the model is not consistent across different levels of the predictor variables, which can make predictions less reliable. Additionally, it was observed that the predictor variables, obesity, and inactivity, were highly correlated. This high correlation could lead to multicollinearity issues in predictive models, making it challenging to determine the unique contribution of each variable in explaining diabetes risk. Addressing multicollinearity through techniques like feature selection or dimensionality reduction may be necessary to build a more robust predictive model. In summary, the CDC data analysis revealed a significant relationship between obesity, inactivity, and diabetes, suggesting that these variables are important in understanding diabetes risk. However, the presence of homoscedasticity and high correlation between the predictor variables should be considered when developing predictive models for diabetes, to ensure the model’s efficiency and accuracy.