What are some common mistakes in linear regression?

What are some common mistakes in linear regression?

Some common mistakes include ignoring multicollinearity, not checking for linearity, and failing to validate the model.

Can you explain multicollinearity and why it's a problem?

Multicollinearity occurs when independent variables are highly correlated, making it hard to determine their individual effects.

How can I detect multicollinearity in my dataset?

You can use Variance Inflation Factor (VIF) or correlation matrices to detect multicollinearity in your dataset.

What should I do if I find multicollinearity?

You can remove one of the correlated variables, combine them, or use techniques like ridge regression to mitigate its effects.

Are there any other common pitfalls I should be aware of?

Yes, also watch out for overfitting, underfitting, and not checking the assumptions of linear regression.