What are the limitations of regression models?

What are the limitations of regression models?

Regression models are powerful, but they have limitations such as assumptions of linearity, sensitivity to outliers, and multicollinearity.

Can you explain the assumption of linearity?

Sure! The assumption of linearity means that the relationship between the independent and dependent variables is linear, which may not always be true.

What happens if this assumption is violated?

If the linearity assumption is violated, the model may produce biased estimates and poor predictions, leading to inaccurate conclusions.

Are there alternatives to regression models?

Yes, alternatives include decision trees, random forests, and neural networks, which can capture non-linear relationships.

How do I choose the right model for my data?

Choosing the right model depends on the data characteristics, the relationship you expect, and the specific problem you're trying to solve.