According to the findings of my multiple linear regression, which I discussed in my previous post, the percentage of inactivity (% INACTIVE) and the percentage of obesity (% OBESE) are both statistically significant predictors of the proportion of people who have diabetes (% DIABETIC). The R-squared value of the model is 0.341, indicating that the model’s predictors can account for approximately 34.1% of the variance in the percentage of diabetics. The total model may be statistically significant, as indicated by the significant F-statistic.

 

Although I also ran the Breusch-Pagan test, which measures heteroscedasticity, the p-value was extremely low (around zero), suggesting that the model contains evidence of heteroscedasticity. The reliability may be impacted by heteroscedasticity, which is when the variance of the residuals varies depending on the values of the predictors, which can affect the reliability of regression estimates.

It is essential to address the situation because heteroscedasticity has been revealed. I will attempt to stabilize the variance by changing the dependent variable (% DIABETIC) using a suitable transformation (e.g., log-transformation) also explore other predictor variables like socioeconomic status, household composition, and disability and then reevaluate the model to determine its explanatory and predictive capacity.