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What are common mistakes to avoid with Linear Regression

Intermediate

Answer

A common mistake is using linear regression on data with a clearly non linear relationship, which leads to poor predictions and misleading conclusions.
Real-world example A real estate company predicts house prices based on square footage, using linear regression to estimate how much price increases for each additional square foot.

Common follow-ups: What is logistic regression, What is R squared, What is multicollinearity

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All 7 Linear Regression questions