Author(s): Ananya

Originally published on Towards AI.

Linear Regression : 33 Must Do Interview Questions

I write articles on Data Science, Finance and philosophy. In this one, I am focusing on one of the popular machine learning algorithms Linear Regression, if you are someone who like reading about these topics feel free to subscribe. These question are more of conceptual based and interview centric.

PS: I like putting doodle images as illustrations, it helps me remember better and also keeps things interesting.

Q1. What is the significance of linear regression ?

Q2. What is the difference between dependent and independent variable ?

Q3. Give the equation for linear regression.

Q4. What assumptions does linear regression make ?

Q5. What is the difference between correlation and regression ?

Q6. What is Residual Sum of Errors ?

Q7. Explain OLS.

Q8. How will you reduce overfitting in a linear regression model ?

Q9. What is heteroscedasticity ?

Q10. What are different ways to assess error of a linear regression model ?

Q11. What is the difference between RMSE, MSE, MAE.

Q12. Explain R squared intutively.

Q13. Why do we use adjusted R squared ?

Q14. How to determine coefficients of a SLR model ?

Q15. Which cases would you prefer linear model over other fancier non linear model ?

Q16. What is the difference between SLS and MLS ?

Q17. What is RSE and how do we interpret it?

Q18. How do you assess goodness of fit for a linear regression model?

Q19. What are the problems of linear regression model and how do we solve for it ?

Q20. Explain Bias Variance Tradoff wrt to LR.

Q21. How do we achieve subset selection in LR ?

Q22. What are different subset selection methods ?

Q23. Discuss limitations of various subset selection methods.

Q24. What are shrinkage methods ?

Q25. Explain Ridge Regression, Lasso Regression and Elastic Net.

Q26. Give real life examples where you will use linear regression.

Q27. What is F Statistic in linear regression ?

Q28. What does P value signify in regression ?

Q29. What is confidence interval for coefficients ?

Q30. What is dummy variable trap ?

Q31. How do you interpret coefficients for categorical variable ?

Q32. What pattern in residual plot indicates model issues?

Q33. How would you interpret missing values in linear regression?

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