While regression models are powerful tools, they come with certain pitfalls and limitations:
Confounding: Failure to adjust for confounding variables can lead to biased estimates. Multicollinearity: High correlation between predictor variables can distort the coefficients and make them unreliable. Overfitting: Including too many variables can lead to overfitting, where the model performs well on the training data but poorly on new data. Assumptions: Each regression model comes with its own set of assumptions. Violation of these assumptions can lead to incorrect inferences.
