juniorLinear Regression
What are assumptions of Linear Regression?
Updated May 16, 2026
Short answer
Linearity, independence, homoscedasticity, normality, and no multicollinearity.
Deep explanation
These assumptions ensure valid statistical inference. Violations can lead to biased or inefficient estimates.
Real-world example
Financial forecasting models rely heavily on these assumptions.
Common mistakes
- Ignoring residual diagnostics.
Follow-up questions
- How do you test homoscedasticity?