Linear regression is a powerful tool in data analysis, but it relies on some crucial assumptions. One of these is homoskedasticity, which means the variance of errors should be constant across different levels of independent variables. If this assumption doesn’t hold, our regression results may not be reliable. This is where Python’s Breusch-Pagan test, available in the statsmodels library.
To detect Heteroskedasticity with the Breusch-Pagan Test in python I used the following steps:
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- Import the necessary libraries, including statsmodels.
- Fit an Ordinary Least Squares (OLS) regression model to your data, specifying the dependent and independent variables.
- Use the het_breuschpagan function from statsmodels to perform the Breusch-Pagan test on the residuals of the regression model.
- The p-value obtained from the Breusch-Pagan test is crucial for identifying heteroskedasticity. If this p-value is below a chosen significance level, typically 0.05, it suggests that heteroskedasticity may be affecting the reliability of your regression analysis.
Linear 3D models:
Whenever we must examinate the relationship between 3 variables in our case %diabetes, %inactivity and %obesity. We can use this model to visualize and understand the intricate relationships among three variables in a three-dimensional space. Variables don’t always act independently. Sometimes, one variable’s effect on the outcome depends on the value of another variable. These interactions can significantly influence your model’s predictions and are crucial to consider.