The knowledge about heteroskedasticity in statistical analysis is primarily provided by this blog. Using the common factor, FIPS, to analyze the data using the three variables of diabetes, obesity, and inactivity. In my statistical analysis of the provided data frames, I got to the conclusion that there is no meaningful evidence of heteroskedasticity.

To do this, I used regression analysis to build a linear regression model, make predictions, then exhibit those predictions as a scatter plot. This observation can be related to my last blog, where I successfully identified the residuals (variance of the mistakes) in the regression model versus the predicted values (fitted values).

I utilized the’statsmodel’ package, which offers classes and functions for estimating and evaluating different statistical models, for this operation. It is frequently used for SM, regression modeling, and statistical analysis.The “least squares” element of the OLS library’s name refers to the Ordinary Least Square (OLS) regression model, which minimizes the sum of the squared differences (residuals) between the observed dependent variable (y) and the values predicted by the linear model